{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":213,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":213,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"1e8bde69c687","filters":{"venue":"IEEE Journal of Biomedical and Health Informatics"}},"results":[{"id":"W2797527544","doi":"10.1109/jbhi.2018.2824327","title":"Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":533,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Checklist; Artificial intelligence; Artificial neural network; Point (geometry); Pattern recognition (psychology); Lesion; Medicine; Pathology; Psychology; Cognitive psychology; Mathematics","authors":[{"name":"Jeremy Kawahara","is_ca":true},{"name":"Sara Daneshvar","is_ca":true},{"name":"Giuseppe Argenziano","is_ca":false},{"name":"Ghassan Hamarneh","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0662949166134811,"gpt":0.3565128103825332,"spread":0.2902178937690521,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001060773,0.001208059,0.000813557,0.001392082,0.0003094415,0.0007390583,0.001629081,0.00144148,0.00224104],"category_scores_gemma":[0.002219561,0.000296781,0.0009904971,0.0007804902,0.0003541987,0.0009126054,0.001321534,0.001073671,0.000918861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008472331,"about_ca_system_score_gemma":0.0007392594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008188979,"about_ca_topic_score_gemma":0.009786477,"domain_scores_codex":[0.9994385,0.0001212708,0.00003363103,0.0001736011,0.0001241348,0.0001088582],"domain_scores_gemma":[0.9991726,0.0002637917,0.0001318826,0.0001282255,0.0002064371,0.00009700016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001222584,0.0009922449,0.03898459,0.0003500916,0.000369329,0.0006432391,0.0002032751,0.3495722,0.01371345,0.002230526,0.02048608,0.5712324],"study_design_scores_gemma":[0.00001732233,0.00009451166,0.003645516,0.00001870773,0.00002615022,0.00009655162,0.00003075871,0.9905409,0.002435944,0.002332866,0.0007453404,0.00001536696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4357584,0.002574783,0.5426885,0.001523043,0.0002780901,0.0003491723,0.004729207,0.005655826,0.00644303],"genre_scores_gemma":[0.9194559,0.0002527723,0.07001845,0.0003425399,0.0001315927,0.0001675069,0.005106684,0.00008368076,0.004440905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008188979,"threshold_uncertainty_score":0.01628268,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3035119815","doi":"10.1109/jbhi.2020.3001216","title":"Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":379,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"National Social Science Fund of China; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; Nanjing Science and Technology Commission; National Science Foundation","keywords":"Computer science; Artificial intelligence; Sentiment analysis; Social media; Recurrent neural network; Coronavirus disease 2019 (COVID-19); Natural language processing; Deep learning; Machine learning; Artificial neural network; Public health; Decision tree; The Internet; Support vector machine; World Wide Web; Medicine; Disease","authors":[{"name":"Hamed Jelodar","is_ca":false},{"name":"Yongli Wang","is_ca":false},{"name":"Rita Orji","is_ca":true},{"name":"Shucheng Huang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2666507334657404,"gpt":0.4049349140857755,"spread":0.1382841806200351,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009390738,0.0007558315,0.0004056606,0.001184194,0.0004246781,0.000688549,0.0004981231,0.0006485231,0.00133839],"category_scores_gemma":[0.002076603,0.0001945182,0.0008300885,0.0006675969,0.0002147004,0.00101152,0.0004888964,0.0009570431,0.0006181799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006600278,"about_ca_system_score_gemma":0.0004884574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004417604,"about_ca_topic_score_gemma":0.005256646,"domain_scores_codex":[0.9995825,0.0001339562,0.0000294661,0.0001040625,0.00005989763,0.00009011727],"domain_scores_gemma":[0.9990569,0.0004928464,0.0001341514,0.0000355251,0.0002409731,0.00003969295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00115199,0.001167245,0.04060876,0.0004661286,0.0003724224,0.0009550811,0.002119079,0.09973584,0.06457489,0.005728298,0.01747351,0.7656468],"study_design_scores_gemma":[0.00001016796,0.00005352331,0.003164538,0.00001026964,0.00003889791,0.00002344201,0.0001748432,0.9900587,0.003833748,0.001684684,0.0009383533,0.000008897466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6692168,0.001092162,0.3165554,0.002060718,0.0003963042,0.0002163286,0.001458169,0.001562696,0.00744142],"genre_scores_gemma":[0.9538018,0.000310897,0.03971899,0.0001572151,0.0002461542,0.00009358583,0.001753962,0.0000460259,0.003871416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004417604,"threshold_uncertainty_score":0.008783758,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2949980035","doi":"10.1109/jbhi.2019.2914970","title":"Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":250,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; H. Lundbeck A/S; Servier; Eisai; AbbVie; Ministerio de Ciencia e Innovación; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; Takeda Pharmaceutical Company; Roche; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Drug Discovery Foundation; Merck; Fujirebio Europe; Alzheimer's Association; Foundation for the National Institutes of Health; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative","keywords":"Artificial intelligence; Pattern recognition (psychology); Autoencoder; Computer science; Deep learning; Principal component analysis; Convolutional neural network; Medical diagnosis; Machine learning; Medicine; Radiology","authors":[{"name":"Francisco J. Martínez-Murcia","is_ca":false},{"name":"Andrés Ortíz","is_ca":false},{"name":"J. M. Górriz","is_ca":false},{"name":"Javier Ramı́rez","is_ca":false},{"name":"Diego Castillo-Barnés","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07445546892299126,"gpt":0.3138618827632492,"spread":0.2394064138402579,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004426537,0.0003968819,0.0003174534,0.0008061189,0.0001713868,0.0003790437,0.0002589604,0.0003508262,0.0002808175],"category_scores_gemma":[0.001026046,0.0002265756,0.0004341288,0.0004612575,0.0004918759,0.0005574832,0.000406195,0.0006195525,0.00005338716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003894197,"about_ca_system_score_gemma":0.0003540432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003559545,"about_ca_topic_score_gemma":0.003089733,"domain_scores_codex":[0.9999105,0.00003136967,0.000004013059,0.00002065487,0.00001942017,0.00001404497],"domain_scores_gemma":[0.999689,0.0001736494,0.00004660215,0.00003575095,0.00003847508,0.00001660452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001069082,0.00009514834,0.01467825,0.00008158238,0.0001807952,0.0002099891,0.0002837284,0.7951247,0.02106741,0.02736547,0.00081402,0.1399921],"study_design_scores_gemma":[0.000001533165,0.000015641,0.002997956,0.000005415106,0.000008200072,0.00002944065,0.00001869628,0.9849866,0.0009591716,0.01073564,0.0002365434,0.000005171744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2101302,0.0008356383,0.7874312,0.0004099331,0.00001393745,0.00001686456,0.000103825,0.000195346,0.000863196],"genre_scores_gemma":[0.9026648,0.0005958246,0.09552469,0.0000472488,0.00002712062,0.00002099786,0.0001445639,0.00002489568,0.0009496874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003559545,"threshold_uncertainty_score":0.007077634,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2072343128","doi":"10.1109/jbhi.2013.2288522","title":"Local Mesh Patterns Versus Local Binary Patterns: Biomedical Image Indexing and Retrieval","year":2014,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":215,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Local binary patterns; Search engine indexing; Image retrieval; Computer science; Pixel; Pattern recognition (psychology); Artificial intelligence; Image (mathematics); Binary image; Database index; Binary number; Computer vision; Image processing; Mathematics; Histogram","authors":[{"name":"Subrahmanyam Murala","is_ca":true},{"name":"Q. M. Jonathan Wu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02862179008186281,"gpt":0.308117239058123,"spread":0.2794954489762602,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007245333,0.0004775442,0.001022188,0.00319461,0.0002542708,0.001228912,0.0009438422,0.00126673,0.002380693],"category_scores_gemma":[0.003306485,0.0002237088,0.0005930456,0.004062016,0.0005757245,0.00249662,0.0007616832,0.000549762,0.001886908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003674457,"about_ca_system_score_gemma":0.0003572472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006321914,"about_ca_topic_score_gemma":0.0006519862,"domain_scores_codex":[0.999056,0.0001394458,0.0001212023,0.0001604185,0.0004603917,0.00006243608],"domain_scores_gemma":[0.9990988,0.0002625064,0.0001462309,0.0001953975,0.0002596152,0.00003748915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003592402,0.00008974849,0.0010878,0.000726165,0.00006523535,0.0002442299,0.00007510461,0.004576204,0.07043377,0.007496358,0.00529107,0.9095551],"study_design_scores_gemma":[0.0002581887,0.001627719,0.01993413,0.0004310651,0.0004802125,0.01039082,0.0005393038,0.6347321,0.2075277,0.04707224,0.07675277,0.0002536865],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02430393,0.006514412,0.9630589,0.0006061093,0.0005227126,0.0002489385,0.0003371464,0.00113902,0.003268832],"genre_scores_gemma":[0.2752263,0.006742005,0.7076979,0.0005855436,0.0007553475,0.0004589854,0.001152897,0.0001761408,0.007204814],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00319461,"threshold_uncertainty_score":0.007964253,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2914410118","doi":"10.1109/jbhi.2018.2865450","title":"Convolutional Neural Network With Shape Prior Applied to Cardiac MRI Segmentation","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":209,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Segmentation; Convolutional neural network; Sørensen–Dice coefficient; Artificial intelligence; Ventricle; Endocardium; Magnetic resonance imaging; Cardiac magnetic resonance imaging; Deep learning; Hausdorff distance; Pattern recognition (psychology); Image segmentation; Computer vision; Medicine; Radiology; Cardiology","authors":[{"name":"Clément Zotti","is_ca":true},{"name":"Zhiming Luo","is_ca":false},{"name":"Alain Lalande","is_ca":false},{"name":"Pierre‐Marc Jodoin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02378500703133865,"gpt":0.3144664781195526,"spread":0.290681471088214,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004446926,0.0007531314,0.0005427687,0.0005408976,0.0002418698,0.0005088346,0.000965381,0.0008845208,0.001200638],"category_scores_gemma":[0.001027673,0.000433708,0.000522177,0.0006652098,0.0003976994,0.0007861142,0.0006381552,0.0008324645,0.0005128928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001122457,"about_ca_system_score_gemma":0.00098016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01728093,"about_ca_topic_score_gemma":0.01642888,"domain_scores_codex":[0.9997873,0.00002532408,0.00001183487,0.00007249734,0.00006160402,0.0000414721],"domain_scores_gemma":[0.999733,0.0000860599,0.00003044123,0.00004429312,0.00008871961,0.00001752474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002279993,0.00008752798,0.001205055,0.00006728934,0.00008013465,0.0001239155,0.00004115811,0.7149634,0.02362758,0.003428397,0.001903957,0.2542437],"study_design_scores_gemma":[0.000002090852,0.00001276131,0.0001724324,0.000003042541,0.000006131102,0.0000165115,0.000001222179,0.9963297,0.002584197,0.0005533277,0.0003155805,0.000003048027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07556079,0.001498684,0.915423,0.0003493571,0.0001140495,0.0000554432,0.0002631576,0.003473525,0.003261976],"genre_scores_gemma":[0.7731705,0.0008592159,0.2172618,0.0002825155,0.00006292692,0.00008114742,0.00081262,0.0001862982,0.007282931],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01728093,"threshold_uncertainty_score":0.03436071,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3018662283","doi":"10.1109/jbhi.2020.2990529","title":"Homecare Robotic Systems for Healthcare 4.0: Visions and Enabling Technologies","year":2020,"lang":"en","type":"review","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":206,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"Japan Society for the Promotion of Science; KDDI Foundation; Natural Science Foundation of Zhejiang Province; McMaster Institute for Research on Aging, McMaster University; Recruitment Program of Global Experts; Zhejiang University; Academy of Finland; National Natural Science Foundation of China; State Key Laboratory of Fluid Power and Mechatronic Systems; McMaster University","keywords":"Vision; Computer science; Health care; Healthcare system; Human–computer interaction","authors":[{"name":"Geng Yang","is_ca":false},{"name":"Zhibo Pang","is_ca":false},{"name":"M. Jamal Deen","is_ca":true},{"name":"Mianxiong Dong","is_ca":false},{"name":"Yuan‐Ting Zhang","is_ca":false},{"name":"Nigel H. Lovell","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1178604091688501,"gpt":0.3835562335213686,"spread":0.2656958243525184,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007336323,0.0008552701,0.0006920802,0.002666025,0.0003339314,0.001394329,0.0008716755,0.001518673,0.003103387],"category_scores_gemma":[0.001074354,0.0003224629,0.0004713968,0.002142408,0.0005582452,0.00254596,0.0008993016,0.001937233,0.002026511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006806805,"about_ca_system_score_gemma":0.001354876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00116631,"about_ca_topic_score_gemma":0.001311975,"domain_scores_codex":[0.9996476,0.00005155991,0.00003346784,0.00005275655,0.0001780503,0.00003656619],"domain_scores_gemma":[0.999447,0.0002643064,0.00005105352,0.00002087496,0.0001780144,0.00003859883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002738312,0.00006539773,0.0002227125,0.007870373,0.00004183175,0.0001697451,0.000102257,0.0007764132,0.001297983,0.03172968,0.02838512,0.9293111],"study_design_scores_gemma":[0.000004856047,0.0001040141,0.0006527507,0.003792306,0.00004444312,0.001081093,0.0001253065,0.0005354957,0.0006125993,0.009154099,0.9838629,0.00003033146],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003139122,0.9886433,0.002309082,0.00106613,0.0006327989,0.00001824409,0.00003160107,0.00004100605,0.006943931],"genre_scores_gemma":[0.002960813,0.9922442,0.001988522,0.0006215904,0.0004413543,0.00002004639,0.0000511123,0.000006747161,0.001665751],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003103387,"threshold_uncertainty_score":0.01038182,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3009259306","doi":"10.1109/jbhi.2020.2978004","title":"A Residual Based Attention Model for EEG Based Sleep Staging","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":169,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Computer science; Electroencephalography; Sleep (system call); Artificial intelligence; Sleep Stages; Deep learning; Convolutional neural network; Context (archaeology); Residual; Sleep medicine; Speech recognition; Machine learning; Pattern recognition (psychology); Polysomnography; Sleep disorder; Cognition; Medicine","authors":[{"name":"Wei Qu","is_ca":false},{"name":"Zhiyong Wang","is_ca":false},{"name":"Hong Hong","is_ca":false},{"name":"Zheru Chi","is_ca":false},{"name":"Dagan Feng","is_ca":false},{"name":"Ronald R. Grunstein","is_ca":false},{"name":"Christopher J. Gordon","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1019287903213148,"gpt":0.3427412885494138,"spread":0.240812498228099,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002858241,0.0006741253,0.0005362174,0.0004222888,0.0002158689,0.0004188053,0.001265411,0.0007036207,0.0028544],"category_scores_gemma":[0.0007128392,0.0002446322,0.000734724,0.0003934203,0.0002688433,0.000585107,0.0005156108,0.0009218016,0.0007167339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005589712,"about_ca_system_score_gemma":0.0006328772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0154058,"about_ca_topic_score_gemma":0.01554465,"domain_scores_codex":[0.9998711,0.00001623306,0.000008006205,0.00005309059,0.00002286161,0.00002865206],"domain_scores_gemma":[0.9998755,0.00003805057,0.00001477946,0.000009755677,0.00005301559,0.000008818227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004443107,0.0002016775,0.003370187,0.0001657948,0.0001531992,0.0003531452,0.0001410576,0.5966202,0.02393418,0.007649055,0.009182404,0.3577848],"study_design_scores_gemma":[0.000006912906,0.0000255161,0.0004764755,0.000005006196,0.00001927882,0.00002523091,0.000003185903,0.9972951,0.0007125904,0.0009652544,0.000459756,0.000005695911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06491334,0.002638359,0.9206679,0.0007402894,0.0003448452,0.00008917259,0.0007061203,0.003078466,0.006821482],"genre_scores_gemma":[0.938738,0.0007127107,0.04782235,0.0003257864,0.0001319066,0.0001218514,0.00083164,0.0001056624,0.01121005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0154058,"threshold_uncertainty_score":0.03063226,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1971513336","doi":"10.1109/jbhi.2013.2274809","title":"A Level-Crossing Based QRS-Detection Algorithm for Wearable ECG Sensors","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":167,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Wearable computer; Computer science; QRS complex; Asynchronous communication; Sensitivity (control systems); CMOS; Wireless sensor network; Algorithm; Wireless; Artificial intelligence; Electronic engineering; Embedded system; Telecommunications; Engineering; Computer network; Medicine","authors":[{"name":"Nassim Ravanshad","is_ca":false},{"name":"Hamidreza Rezaee-Dehsorkh","is_ca":false},{"name":"Reza Lotfi","is_ca":false},{"name":"Yong Lian","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05944575197160708,"gpt":0.3446547883872054,"spread":0.2852090364155984,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004026092,0.0004370219,0.0003742554,0.0005660016,0.0001580397,0.0005240224,0.0007463362,0.0004406014,0.002184239],"category_scores_gemma":[0.001221505,0.0001964664,0.0002594262,0.0005655206,0.0001452083,0.0006283796,0.0002976076,0.0003985629,0.0009202241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002480846,"about_ca_system_score_gemma":0.0002499946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003356836,"about_ca_topic_score_gemma":0.000523161,"domain_scores_codex":[0.9996111,0.00005461798,0.00003599646,0.0001077144,0.0001734937,0.00001714641],"domain_scores_gemma":[0.9996387,0.0001147881,0.0000457804,0.00004106399,0.0001400351,0.00001973476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003599747,0.00007849358,0.001857086,0.0002405771,0.00006514698,0.0001715076,0.00006857127,0.007809837,0.2733068,0.003222558,0.003236956,0.7095825],"study_design_scores_gemma":[0.0001193398,0.001329342,0.01085992,0.00006426981,0.000170759,0.002561521,0.00004957938,0.650382,0.2995782,0.00355223,0.03124384,0.00008890226],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0144514,0.0007139763,0.9820921,0.00005749179,0.00006918439,0.0000644707,0.00009758395,0.001658004,0.0007956823],"genre_scores_gemma":[0.1930597,0.0006693232,0.8034557,0.0001975156,0.0000932809,0.000101227,0.0003718552,0.0001133052,0.001938127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002184239,"threshold_uncertainty_score":0.007306993,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2343237073","doi":"10.1109/jbhi.2016.2519686","title":"Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":161,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada; Office of Science; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; U.S. Department of Energy","keywords":"Computer science; Segmentation; Artificial intelligence; Algorithm; Image segmentation; Pattern recognition (psychology)","authors":[{"name":"Zhi Lu","is_ca":false},{"name":"Gustavo Carneiro","is_ca":false},{"name":"Andrew P. Bradley","is_ca":false},{"name":"Daniela Ushizima","is_ca":false},{"name":"Masoud S. Nosrati","is_ca":true},{"name":"Andrea Bianchi","is_ca":false},{"name":"Cláudia Martins Carneiro","is_ca":false},{"name":"Ghassan Hamarneh","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1109182972637847,"gpt":0.386238031748342,"spread":0.2753197344845573,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006303205,0.002468251,0.002381972,0.004945022,0.001470342,0.004861091,0.00344824,0.005463315,0.002097792],"category_scores_gemma":[0.01906189,0.0007623048,0.002395827,0.002845089,0.001098198,0.001822473,0.001898039,0.001606836,0.001440425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002408835,"about_ca_system_score_gemma":0.003095918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01247402,"about_ca_topic_score_gemma":0.01281301,"domain_scores_codex":[0.9930891,0.001142595,0.0007385752,0.001564833,0.002845097,0.0006197685],"domain_scores_gemma":[0.9922122,0.003350041,0.0004438079,0.0009014661,0.002649693,0.0004427751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003383975,0.000869929,0.01098169,0.001894838,0.001260299,0.0003705833,0.0005245901,0.1953657,0.03611703,0.002778544,0.01133724,0.7351156],"study_design_scores_gemma":[0.0002300714,0.001031808,0.007975625,0.0001297358,0.0003035203,0.0007697956,0.0003674511,0.9311611,0.04869147,0.001672131,0.007567387,0.00009996303],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2851333,0.01214114,0.6683677,0.001800845,0.001330367,0.001875714,0.002782875,0.01651729,0.01005072],"genre_scores_gemma":[0.3529959,0.002384838,0.6316487,0.000620256,0.0001789508,0.0004853418,0.006964278,0.001423274,0.003298369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01247402,"threshold_uncertainty_score":0.03333491,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4210894459","doi":"10.1109/jbhi.2022.3149288","title":"Federated Machine Learning for Detection of Skin Diseases and Enhancement of Internet of Medical Things (IoMT) Security","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":156,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; King Saud University","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Deep learning; Machine learning; Medical imaging; Pattern recognition (psychology)","authors":[{"name":"Md. Nazmul Hossen","is_ca":false},{"name":"Vijayakumari Panneerselvam","is_ca":false},{"name":"Deepika Koundal","is_ca":false},{"name":"Kawsar Ahmed","is_ca":true},{"name":"Francis M. Bui","is_ca":true},{"name":"Sobhy M. Ibrahim","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0171938633717467,"gpt":0.2999379703387757,"spread":0.282744106967029,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001203291,0.0004699238,0.0004995605,0.0007357151,0.0002252307,0.0005882658,0.0005873207,0.0006049025,0.0005871797],"category_scores_gemma":[0.001998974,0.0001193964,0.0005305166,0.000395509,0.0002663108,0.0008396463,0.0005190285,0.0005038514,0.0002169259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006729378,"about_ca_system_score_gemma":0.0004919692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002374922,"about_ca_topic_score_gemma":0.001963673,"domain_scores_codex":[0.999465,0.0001620941,0.00003392795,0.0001227083,0.0001396583,0.00007651785],"domain_scores_gemma":[0.9993429,0.0002075486,0.00007123111,0.0001600948,0.0001856386,0.00003258537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006683519,0.0006360576,0.01658641,0.0001493208,0.0002088836,0.0005199963,0.00008679135,0.2816664,0.02552866,0.005038864,0.006683985,0.6622263],"study_design_scores_gemma":[0.000006721677,0.00008970546,0.002084682,0.00001237198,0.00002593926,0.0001355886,0.00002050362,0.984174,0.01010945,0.002360768,0.0009712341,0.000009064899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.326987,0.002314015,0.6597223,0.001095367,0.0003141406,0.0001361888,0.0004007488,0.003985956,0.005044324],"genre_scores_gemma":[0.9571161,0.0003487003,0.04054192,0.0001828445,0.00003153706,0.00003326197,0.0003145463,0.00001820771,0.001412912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002374922,"threshold_uncertainty_score":0.00636363,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2978639826","doi":"10.1109/jbhi.2019.2944643","title":"Deep Learning-Based Gleason Grading of Prostate Cancer From Histopathology Images—Role of Multiscale Decision Aggregation and Data Augmentation","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"AI in cancer detection","field":"Computer Science","cited_by":150,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Prostate Cancer Canada","keywords":"Histopathology; Prostate cancer; Grading (engineering); Artificial intelligence; Prostate; Computer science; Medicine; Radiology; Medical physics; Cancer; Pathology; Internal medicine","authors":[{"name":"Davood Karimi","is_ca":true},{"name":"Guy Nir","is_ca":true},{"name":"Ladan Fazli","is_ca":true},{"name":"Peter C. Black","is_ca":true},{"name":"Larry Goldenberg","is_ca":true},{"name":"Septimiu E. Salcudean","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02124589333563531,"gpt":0.3210817892733692,"spread":0.2998358959377339,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00133428,0.0006356321,0.0006243349,0.0005867881,0.0001630948,0.0007349559,0.001047573,0.0005082989,0.0008220933],"category_scores_gemma":[0.0039708,0.0003032268,0.0006455225,0.0004207607,0.0003662739,0.001169205,0.001071687,0.001040202,0.000313906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005551645,"about_ca_system_score_gemma":0.0006245515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001877888,"about_ca_topic_score_gemma":0.002274243,"domain_scores_codex":[0.9994909,0.0001202029,0.00003809147,0.0001278183,0.0001744252,0.00004855507],"domain_scores_gemma":[0.9989039,0.0004632497,0.0001744919,0.0001794778,0.0002197868,0.00005908276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003865955,0.0002826101,0.006756306,0.0001944842,0.00009535849,0.0001593543,0.0001137757,0.2280726,0.06767649,0.003678919,0.003229188,0.6893542],"study_design_scores_gemma":[0.000006428776,0.00003929898,0.0009363003,0.000009666699,0.00001404139,0.00003759488,0.00000704905,0.9866262,0.01043562,0.001392083,0.0004848597,0.0000108168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1368597,0.0007057897,0.8570225,0.0007224905,0.0001145683,0.000113398,0.0002356361,0.002739171,0.001486826],"genre_scores_gemma":[0.6843714,0.0004078878,0.3129469,0.0002571612,0.0000924941,0.0001357229,0.0005350181,0.0001242852,0.001129028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001877888,"threshold_uncertainty_score":0.007056415,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3158731422","doi":"10.1109/jbhi.2021.3075995","title":"An Efficient Ciphertext-Policy Weighted Attribute-Based Encryption for the Internet of Health Things","year":2021,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brandon University","funders":"Japan Society for the Promotion of Science; National Natural Science Foundation of China","keywords":"Encryption; Scheme (mathematics); The Internet; Internet of Things; Security analysis; Attribute-based encryption; Coding (social sciences); Cryptography","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.03604034266255726,"gpt":0.330014678386462,"spread":0.2939743357239047,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001186733,0.0003570321,0.0005976458,0.000519418,0.0007571952,0.0009533996,0.0009951034,0.0005589736,0.00133921],"category_scores_gemma":[0.002866499,0.0001865505,0.0005860534,0.001133524,0.0006965755,0.002993437,0.001777062,0.001216258,0.0005564375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008764572,"about_ca_system_score_gemma":0.001242906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005581668,"about_ca_topic_score_gemma":0.0004599454,"domain_scores_codex":[0.997856,0.0005714053,0.0002114599,0.0002448988,0.0008858936,0.0002303457],"domain_scores_gemma":[0.9987895,0.0003027751,0.0001748572,0.0003988221,0.0002581204,0.00007588207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009336757,0.0002960286,0.001549583,0.0004248976,0.0001069587,0.001159594,0.0007777523,0.06591012,0.1166326,0.5729099,0.0122932,0.2270057],"study_design_scores_gemma":[0.000228598,0.0005205336,0.001237725,0.0001077624,0.0001095354,0.002536563,0.0002288703,0.7396762,0.09016244,0.1245272,0.04046286,0.000201727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02964826,0.0004343597,0.9641164,0.0005479211,0.0001676832,0.0002657348,0.000185308,0.0003141093,0.004320216],"genre_scores_gemma":[0.7762978,0.0005682031,0.2159097,0.0003163269,0.0001078539,0.000259365,0.000380214,0.00004646683,0.006114019],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00133921,"threshold_uncertainty_score":0.00635916,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3091542279","doi":"10.1109/jbhi.2020.3027967","title":"Fall Detection With UWB Radars and CNN-LSTM Architecture","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":133,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Convolutional neural network; Context (archaeology); Artificial intelligence; Deep learning; Artificial neural network","authors":[{"name":"Julien Maítre","is_ca":true},{"name":"Kévin Bouchard","is_ca":true},{"name":"Sébastien Gaboury","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01660723541950107,"gpt":0.2294419070945501,"spread":0.212834671675049,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003029867,0.0009572378,0.0005141569,0.0005295509,0.0002082074,0.0004122877,0.0009809484,0.0008652268,0.002261496],"category_scores_gemma":[0.0007576816,0.0005176727,0.0005560017,0.0005353433,0.0001816357,0.0006825412,0.0005622604,0.0008336926,0.00119637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005798866,"about_ca_system_score_gemma":0.0005581189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009303009,"about_ca_topic_score_gemma":0.009636851,"domain_scores_codex":[0.9998374,0.00001829992,0.000009097957,0.00005991816,0.00003944743,0.00003586725],"domain_scores_gemma":[0.9998771,0.00002641176,0.00001653132,0.00001741032,0.00005097354,0.00001143248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005244223,0.0003611123,0.003195243,0.000226896,0.0002228476,0.0002760477,0.0000710817,0.2471266,0.04151217,0.001818291,0.008118261,0.696547],"study_design_scores_gemma":[0.000007277141,0.0000593385,0.0008909684,0.00001326852,0.00002525395,0.00006024703,0.00000833886,0.9928361,0.004819418,0.0006307606,0.0006401039,0.000009016106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1573209,0.002928922,0.81912,0.0008832593,0.0005262546,0.0001408902,0.001023584,0.01092965,0.007126396],"genre_scores_gemma":[0.8140239,0.0009086397,0.1745643,0.0003491494,0.000122856,0.0001344136,0.001389456,0.0001059862,0.008401375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009303009,"threshold_uncertainty_score":0.01849771,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1548832181","doi":"10.1109/jbhi.2015.2432832","title":"Feature Selection Based on the SVM Weight Vector for Classification of Dementia","year":2015,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":123,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research","keywords":"Support vector machine; Feature selection; Artificial intelligence; Pattern recognition (psychology); Voxel; Computer science; Dementia; Feature (linguistics); Selection (genetic algorithm); Kernel (algebra); Feature vector; Feature extraction; Neuroimaging; Mathematics; Medicine; Disease; Pathology","authors":[{"name":"Esther E. Bron","is_ca":false},{"name":"Marion Smits","is_ca":false},{"name":"Wiro J. Niessen","is_ca":false},{"name":"Stefan Klein","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07899907698726526,"gpt":0.3756823318387058,"spread":0.2966832548514405,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00206682,0.0008568909,0.001167032,0.002098041,0.0002393284,0.0006263968,0.0004399624,0.0005385487,0.0009038101],"category_scores_gemma":[0.00539599,0.0002335993,0.0009548727,0.001415255,0.0002040484,0.0004917303,0.0003796536,0.0006264927,0.0004322867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000365448,"about_ca_system_score_gemma":0.0005511879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002036157,"about_ca_topic_score_gemma":0.001323157,"domain_scores_codex":[0.9990799,0.000293378,0.0001068172,0.0001608396,0.0002661473,0.00009284629],"domain_scores_gemma":[0.9982384,0.001082018,0.0001278232,0.0000881834,0.000420276,0.00004324992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008033643,0.0002852373,0.0177536,0.0002064077,0.0002833806,0.0002290738,0.00010078,0.05231906,0.03570458,0.0006475333,0.003581322,0.8880857],"study_design_scores_gemma":[0.00008284508,0.0004936432,0.02714477,0.00003930957,0.0001169923,0.0002911931,0.00005185282,0.9480355,0.02040962,0.001726575,0.00154673,0.00006094485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2433023,0.001087878,0.750914,0.0001659173,0.0001250929,0.0002316741,0.0004873169,0.002989589,0.0006961731],"genre_scores_gemma":[0.8024699,0.0002468509,0.1951782,0.00005916442,0.00005689668,0.0003007975,0.0009562944,0.00008461572,0.0006472833],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002098041,"threshold_uncertainty_score":0.01093054,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2887983676","doi":"10.1109/jbhi.2018.2864335","title":"Multiday Evaluation of Techniques for EMG-Based Classification of Hand Motions","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":122,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Higher Education Commision, Pakistan; National University of Sciences and Technology; Higher Education Commission, Pakistan","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Computer vision","authors":[{"name":"Asim Waris","is_ca":false},{"name":"Imran Khan Niazi","is_ca":false},{"name":"Mohsin Jamil","is_ca":false},{"name":"Kevin Englehart","is_ca":true},{"name":"Winnie Jensen","is_ca":false},{"name":"Ernest Nlandu Kamavuako","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0765987558424525,"gpt":0.3549882992637897,"spread":0.2783895434213372,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003328581,0.0005137775,0.0005016486,0.0008104275,0.0002637146,0.0005255307,0.0004726751,0.0006566864,0.0005063354],"category_scores_gemma":[0.00620544,0.0001633916,0.0005512389,0.0005410781,0.0002939874,0.0006196175,0.000637136,0.0003980956,0.0003755891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002178483,"about_ca_system_score_gemma":0.000150298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005936307,"about_ca_topic_score_gemma":0.001307611,"domain_scores_codex":[0.9978454,0.0007206252,0.0002061922,0.0004073354,0.0007316332,0.0000887722],"domain_scores_gemma":[0.99295,0.00323663,0.000688764,0.0006141429,0.002350183,0.0001603187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003911297,0.00135038,0.1121485,0.0009817295,0.0008540381,0.0001539474,0.001107278,0.01550385,0.3494604,0.0001906321,0.0005533447,0.5137845],"study_design_scores_gemma":[0.00005764026,0.02111251,0.7003667,0.00009493109,0.0005733256,0.001119529,0.0009157988,0.1248679,0.1482718,0.0001997794,0.002273857,0.0001462235],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9641961,0.001266778,0.03349229,0.00004253662,0.00007659789,0.00009811102,0.0001598133,0.0001106941,0.0005571492],"genre_scores_gemma":[0.969424,0.0005961718,0.02835725,0.00002216743,0.00005145856,0.0001108083,0.0003164928,0.00002785749,0.001093869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003328581,"threshold_uncertainty_score":0.0176034,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2914492151","doi":"10.1109/jbhi.2019.2899070","title":"Artificial Neural Network for in-Bed Posture Classification Using Bed-Sheet Pressure Sensors","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Pressure Ulcer Prevention and Management","field":"Health Professions","cited_by":120,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; Canadian Sleep & Circadian Network","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Supine position; Artificial intelligence; Computer science; Backpropagation; Artificial neural network; Computer vision; Pattern recognition (psychology); Generalization; Mathematics; Medicine","authors":[{"name":"Georges Matar","is_ca":true},{"name":"Jean‐Marc Lina","is_ca":true},{"name":"Georges Kaddoum","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1086958538830762,"gpt":0.4241171413270671,"spread":0.3154212874439909,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005514917,0.0008046531,0.0005701165,0.0004485783,0.0002408302,0.0005221193,0.000730043,0.0008936867,0.001307527],"category_scores_gemma":[0.001074909,0.0002677201,0.0005041593,0.000431612,0.0001852167,0.0004054058,0.0003522901,0.000781978,0.0004944552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003775512,"about_ca_system_score_gemma":0.0004054371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006076027,"about_ca_topic_score_gemma":0.00422675,"domain_scores_codex":[0.999757,0.00005383422,0.00002068312,0.00007622319,0.00005453632,0.00003762513],"domain_scores_gemma":[0.999725,0.0001119969,0.00003355397,0.00001810336,0.00009704406,0.00001418527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000592083,0.0007570817,0.01039712,0.0002106142,0.0001652402,0.000304129,0.0001208542,0.4598381,0.01712801,0.0006175615,0.002935246,0.506934],"study_design_scores_gemma":[0.000004266064,0.00006231675,0.001184396,0.000007857292,0.0000099419,0.00001347771,0.00001023003,0.9973835,0.001059765,0.0001253106,0.0001340238,0.000004867071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3746314,0.002667827,0.6119277,0.0005036689,0.0004330058,0.000237513,0.0005161611,0.002980486,0.006102256],"genre_scores_gemma":[0.9418224,0.0004591167,0.05249663,0.0001448355,0.0000553938,0.0002014457,0.0004875624,0.00002756813,0.00430509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006076027,"threshold_uncertainty_score":0.01208133,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3017890102","doi":"10.1109/jbhi.2020.2984355","title":"Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":120,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"H2020 Marie Skłodowska-Curie Actions; Janssen Alzheimer Immunotherapy Research And Development; Johnson and Johnson Pharmaceutical Research and Development; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Novartis Pharmaceuticals Corporation; Biogen; BioClinica; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; University of Southern California; Engineering and Physical Sciences Research Council; Bristol-Myers Squibb; F. Hoffmann-La Roche; Alzheimer's Drug Discovery Foundation; Alzheimer's Association; Horizon 2020 Framework Programme; Foundation for the National Institutes of Health","keywords":"Disease; Biomarker; Support vector machine; Feature selection; Computer science; Amyloid beta; Machine learning; Alzheimer's disease; Apolipoprotein E; Medicine; Artificial intelligence; Bioinformatics; Computational biology; Pathology; Biology","authors":[{"name":"Chima Eke","is_ca":false},{"name":"Emmanuel Jammeh","is_ca":false},{"name":"Xinzhong Li","is_ca":false},{"name":"Camille Carroll","is_ca":false},{"name":"Stephen Pearson","is_ca":false},{"name":"Emmanuel Ifeachor","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05468688520294072,"gpt":0.3336398252890928,"spread":0.278952940086152,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001655169,0.001084251,0.0009209395,0.001339711,0.0001763924,0.0007072797,0.0004682051,0.0006695093,0.0005393557],"category_scores_gemma":[0.003057636,0.0002022916,0.0007381873,0.0006919605,0.0001962464,0.0007676368,0.0003884092,0.0007810486,0.0003995101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003134126,"about_ca_system_score_gemma":0.0004357435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001315144,"about_ca_topic_score_gemma":0.0009862882,"domain_scores_codex":[0.9993203,0.0002166254,0.00005782948,0.0001593671,0.0001509656,0.00009506405],"domain_scores_gemma":[0.9987873,0.000687537,0.0001752296,0.00005579052,0.0002347825,0.00005923783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001276681,0.0006962332,0.0623089,0.0003167481,0.0003210181,0.0004041804,0.0001407378,0.1315761,0.05644808,0.001219259,0.003904294,0.7413878],"study_design_scores_gemma":[0.00001593673,0.0002044622,0.007743167,0.00001914186,0.00003585439,0.00009272529,0.000029716,0.9804595,0.00986913,0.000946435,0.000559594,0.00002441634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5057135,0.002689212,0.4862062,0.0004757584,0.0001988408,0.0001615543,0.0004886612,0.002564711,0.001501488],"genre_scores_gemma":[0.9146779,0.0003648462,0.08386346,0.0000789168,0.00005050328,0.00007241357,0.0002998135,0.00001943621,0.0005727331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001655169,"threshold_uncertainty_score":0.008753538,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3049320518","doi":"10.1109/jbhi.2020.3016306","title":"Multi-Receptive-Field CNN for Semantic Segmentation of Medical Images","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Higher Education Discipline Innovation Project; Natural Science Foundation of Hunan Province; Hunan Provincial Science and Technology Department; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Concatenation (mathematics); Convolutional neural network; Segmentation; Image segmentation; Receptive field; Feature (linguistics); Pattern recognition (psychology); Context (archaeology); Subnet; Field (mathematics); Encoder; Scale-space segmentation; Computer vision; Feature extraction; Context model; Mathematics","authors":[{"name":"Liangliang Liu","is_ca":false},{"name":"Fang‐Xiang Wu","is_ca":true},{"name":"Yu‐Ping Wang","is_ca":false},{"name":"Jianxin Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07036109749813316,"gpt":0.3782722430124854,"spread":0.3079111455143522,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005147682,0.0007874945,0.0004972458,0.001055547,0.0001797805,0.0004061255,0.000789308,0.0009331946,0.00232655],"category_scores_gemma":[0.0008544608,0.0003218695,0.0007595406,0.0006685998,0.000253569,0.0007982099,0.0005383806,0.0005397227,0.0007632334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000682988,"about_ca_system_score_gemma":0.000765281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00624675,"about_ca_topic_score_gemma":0.00968074,"domain_scores_codex":[0.9997663,0.00003233796,0.00001328063,0.00008406342,0.00005860475,0.00004539459],"domain_scores_gemma":[0.9998676,0.00003519193,0.00002096985,0.0000220166,0.00003913737,0.00001512208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008300111,0.0002271256,0.004648101,0.0004085751,0.000240424,0.0006013541,0.000107288,0.1724229,0.1401358,0.004458262,0.01238224,0.663538],"study_design_scores_gemma":[0.000026415,0.0001181121,0.003279638,0.00004218734,0.00007476249,0.0005034645,0.00003508628,0.953979,0.03400455,0.003650805,0.004257402,0.00002866686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.116002,0.005338101,0.8624513,0.0006189605,0.0002217645,0.0001769766,0.001837813,0.006135878,0.007217212],"genre_scores_gemma":[0.719394,0.001607339,0.2690364,0.0005837238,0.0001072712,0.0001060682,0.003460302,0.0002734744,0.005431358],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00624675,"threshold_uncertainty_score":0.01242077,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2559989326","doi":"10.1109/jbhi.2016.2637342","title":"Automated Detection and Segmentation of Vascular Structures of Skin Lesions Seen in Dermoscopy, With an Application to Basal Cell Carcinoma Classification","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":100,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Basal cell carcinoma; Segmentation; Pathology; Image segmentation; Medicine; Computer science; Carcinoma; Artificial intelligence; Basal cell; Skin cancer; Radiology; Cancer; Internal medicine","authors":[{"name":"Pegah Kharazmi","is_ca":true},{"name":"Mohammed I. AlJasser","is_ca":true},{"name":"Harvey Lui","is_ca":true},{"name":"Z. Jane Wang","is_ca":true},{"name":"Tim K. Lee","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01859195403678745,"gpt":0.3001209467240548,"spread":0.2815289926872673,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006666951,0.0005070621,0.0005691908,0.001908311,0.0003210999,0.0006384858,0.000425183,0.0007347651,0.0004197336],"category_scores_gemma":[0.001182509,0.0002681444,0.0004166398,0.0007650538,0.0002329101,0.0003389142,0.0002808612,0.0003578388,0.0003679366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003589595,"about_ca_system_score_gemma":0.0005269239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00490212,"about_ca_topic_score_gemma":0.006468011,"domain_scores_codex":[0.999545,0.00006818137,0.00002765922,0.0001456017,0.0001512707,0.00006223076],"domain_scores_gemma":[0.9994311,0.0001453451,0.00009979279,0.00007073697,0.0002064637,0.0000465002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005787735,0.0004170683,0.03045185,0.0002802467,0.0001408346,0.0005454688,0.0001665952,0.03357011,0.3549018,0.0005001795,0.00287129,0.5755758],"study_design_scores_gemma":[0.00002827551,0.0003264142,0.07989901,0.00003506616,0.00008408791,0.001679922,0.0001682218,0.7842867,0.1302945,0.0006813444,0.002472259,0.00004421324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6029642,0.002377634,0.3887759,0.0001515204,0.00004987981,0.0002209268,0.0006724242,0.003543761,0.001243739],"genre_scores_gemma":[0.7791446,0.0008143865,0.2177516,0.00004501145,0.00002525041,0.00005748756,0.0009960715,0.00008121756,0.001084283],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00490212,"threshold_uncertainty_score":0.009747207,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3034046925","doi":"10.1109/jbhi.2020.2999638","title":"Deep Matrix Factorization Improves Prediction of Human CircRNA-Disease Associations","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Circular RNAs in diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":99,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Higher Education Discipline Innovation Project; Hunan Provincial Science and Technology Department; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Matrix decomposition; GRASP; Disease; Machine learning; Data mining; Computational biology; Biology; Medicine; Pathology","authors":[{"name":"Chengqian Lu","is_ca":false},{"name":"Min Zeng","is_ca":false},{"name":"Fuhao Zhang","is_ca":false},{"name":"Fang‐Xiang Wu","is_ca":true},{"name":"Min Li","is_ca":false},{"name":"Jianxin Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02974778561721024,"gpt":0.3199646706059299,"spread":0.2902168849887196,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001549976,0.00108935,0.0009067631,0.001329231,0.0004504675,0.0006467877,0.0005547852,0.001200623,0.00135494],"category_scores_gemma":[0.004590136,0.0003585193,0.0009856748,0.0005785705,0.0004335639,0.0006971509,0.0007801511,0.001510444,0.0006138863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005811221,"about_ca_system_score_gemma":0.001090021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01034916,"about_ca_topic_score_gemma":0.01071961,"domain_scores_codex":[0.9992567,0.0002261848,0.00004218857,0.00028503,0.00009791499,0.00009200193],"domain_scores_gemma":[0.9974775,0.001736296,0.0002201513,0.0001486702,0.0002842972,0.0001331815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0019677,0.0007099404,0.1130794,0.0004399464,0.0006832217,0.0008491386,0.0002895835,0.3671178,0.02071336,0.005963305,0.02774867,0.460438],"study_design_scores_gemma":[0.00003153754,0.0000589855,0.002666136,0.00001464515,0.0000265356,0.00009961539,0.00001344054,0.9923017,0.001105832,0.002990343,0.0006799794,0.00001123961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4254595,0.008260667,0.5536858,0.001736248,0.000353443,0.0001507798,0.003077676,0.004192203,0.003083759],"genre_scores_gemma":[0.8969551,0.0009277907,0.09491715,0.0005696783,0.0001876819,0.00007371404,0.004393927,0.00008964289,0.001885271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01034916,"threshold_uncertainty_score":0.02057785,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2575820504","doi":"10.1109/jbhi.2017.2652449","title":"Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge","year":2017,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":92,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"National Heart, Lung, and Blood Institute; Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; Medical Research Council; Auckland Medical Research Foundation; British Heart Foundation","keywords":"Receiver operating characteristic; Statistical model; Artificial intelligence; Myocardial infarction; Computer science; Ventricle; Test set; Pattern recognition (psychology); Set (abstract data type); Statistical power; Statistical hypothesis testing; Medicine; Machine learning; Cardiology; Statistics; Mathematics","authors":[{"name":"Avan Suinesiaputra","is_ca":false},{"name":"Jan D’hooge","is_ca":false},{"name":"Nicolás Duchateau","is_ca":false},{"name":"Jan Ehrhardt","is_ca":false},{"name":"Alejandro F. Frangi","is_ca":false},{"name":"Ali Gooya","is_ca":false},{"name":"Vicente Grau","is_ca":false},{"name":"Karim Lekadir","is_ca":false},{"name":"Allen Lu","is_ca":false},{"name":"Anirban Mukhopadhyay","is_ca":false},{"name":"İlkay Öksüz","is_ca":false},{"name":"Pierre Ablin","is_ca":true},{"name":"Nripesh Parajuli","is_ca":false},{"name":"Xavier Pennec","is_ca":false},{"name":"Marco Pereañez","is_ca":false},{"name":"Catarina Pinto","is_ca":false},{"name":"Paolo Piras","is_ca":false},{"name":"Marc-Michel Rohe","is_ca":false},{"name":"Daniel Rueckert","is_ca":false},{"name":"Dennis Säring","is_ca":false},{"name":"Maxime Sermesant","is_ca":false},{"name":"Kaleem Siddiqi","is_ca":true},{"name":"Xènia Albà","is_ca":false},{"name":"Mahdi Tabassian","is_ca":false},{"name":"Luciano Teresi","is_ca":false},{"name":"Sotirios A. Tsaftaris","is_ca":false},{"name":"Matthias Wilms","is_ca":false},{"name":"Alistair A. Young","is_ca":false},{"name":"Xing-Yu Zhang","is_ca":false},{"name":"Pau Medrano−Gracia","is_ca":false},{"name":"Martino Alessandrini","is_ca":false},{"name":"Jack Allen","is_ca":false},{"name":"Wenjia Bai","is_ca":false},{"name":"Serkan Çimen","is_ca":false},{"name":"Peter Claes","is_ca":false},{"name":"Brett R. Cowan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07527080319049399,"gpt":0.3446236792335592,"spread":0.2693528760430652,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008196128,0.001464035,0.002179964,0.001669045,0.0009073188,0.00218675,0.002357031,0.003286044,0.001009353],"category_scores_gemma":[0.01860461,0.0003911005,0.001750629,0.001352006,0.0008854725,0.001127829,0.002293232,0.002111576,0.001186803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001228798,"about_ca_system_score_gemma":0.001545154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004041951,"about_ca_topic_score_gemma":0.003800515,"domain_scores_codex":[0.9950776,0.001394264,0.0004084138,0.001086024,0.001699319,0.0003344521],"domain_scores_gemma":[0.9861019,0.008097655,0.0007104545,0.002120415,0.002228096,0.0007414272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001870521,0.001620038,0.08299164,0.001074942,0.0005718851,0.001345477,0.001303636,0.1164877,0.01876542,0.004580903,0.06887271,0.7005151],"study_design_scores_gemma":[0.0002160449,0.001063539,0.07614455,0.0002387327,0.000137481,0.002191662,0.001455465,0.858089,0.01919055,0.017595,0.02347679,0.0002010885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8177221,0.005879955,0.145804,0.008313627,0.0008861593,0.00082422,0.01076347,0.003147888,0.006658435],"genre_scores_gemma":[0.8942796,0.001000879,0.08353145,0.0008735569,0.000363117,0.0004184312,0.01677365,0.0002481902,0.00251116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008196128,"threshold_uncertainty_score":0.04334581,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2973069650","doi":"10.1109/jbhi.2019.2937558","title":"A Hierarchical Neural Network for Sleep Stage Classification Based on Comprehensive Feature Learning and Multi-Flow Sequence Learning","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":92,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; China Postdoctoral Science Foundation","keywords":"Computer science; Artificial intelligence; Artificial neural network; Polysomnography; Sleep Stages; Feature (linguistics); Recurrent neural network; Deep learning; Pattern recognition (psychology); Machine learning; Feature extraction; Electroencephalography","authors":[{"name":"Chenglu Sun","is_ca":false},{"name":"Chen Chen","is_ca":false},{"name":"Wei Li","is_ca":false},{"name":"Jiahao Fan","is_ca":false},{"name":"Wei Chen","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08018510914035216,"gpt":0.3515470714976601,"spread":0.271361962357308,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006165113,0.0007967679,0.0005527486,0.0007036592,0.000296314,0.000374539,0.0008536925,0.000623224,0.001051859],"category_scores_gemma":[0.0009490788,0.0002796521,0.0006574995,0.0004793594,0.0002035597,0.0008575174,0.0004857551,0.0006505033,0.0003149726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006170398,"about_ca_system_score_gemma":0.0006801799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01118988,"about_ca_topic_score_gemma":0.01149029,"domain_scores_codex":[0.9997537,0.00004161706,0.00001791673,0.0000959432,0.00005172762,0.00003913829],"domain_scores_gemma":[0.9997873,0.0000664175,0.00002634119,0.00002119202,0.00008432478,0.00001439481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002421501,0.0002891954,0.003894113,0.0001118892,0.0001542325,0.0001085957,0.00009566245,0.3107451,0.01797336,0.002509206,0.002826111,0.6610504],"study_design_scores_gemma":[0.000005326336,0.00005465728,0.0006654817,0.000004674574,0.00001823918,0.00001494287,0.000004255092,0.997421,0.001126703,0.0004778306,0.0001994888,0.000007378479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05377595,0.0008432145,0.9421099,0.0001360774,0.00008617758,0.0001184732,0.0001500612,0.00129763,0.001482437],"genre_scores_gemma":[0.7748367,0.0004294405,0.2200778,0.0001649014,0.00007585936,0.0002509235,0.0005655127,0.00006037144,0.003538385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01118988,"threshold_uncertainty_score":0.02224952,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2800289050","doi":"10.1109/jbhi.2018.2834317","title":"Early Detection of Mild Cognitive Impairment With In-Home Monitoring Sensor Technologies Using Functional Measures: A Systematic Review","year":2018,"lang":"en","type":"review","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":92,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Health and Social Services Centre University Institute of Geriatrics of Sherbrooke; Université Laval; Université de Sherbrooke; Université de Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"Fonds de Recherche du Québec - Santé; Réseau québécois de recherche sur le vieillissement","keywords":"CINAHL; Activities of daily living; Novelty; Dementia; Quality of life (healthcare); Cognition; Systematic review; MEDLINE; Medicine; Psychological intervention; Population; Gerontology; Physical medicine and rehabilitation; Computer science; Psychology; Physical therapy; Disease; Psychiatry; Pathology; Nursing","authors":[{"name":"Maxime Lussier","is_ca":true},{"name":"Monica Lavoie","is_ca":true},{"name":"Sylvain Giroux","is_ca":true},{"name":"Charles Consel","is_ca":false},{"name":"Manon Guay","is_ca":true},{"name":"Joël Macoir","is_ca":true},{"name":"Carol Hudon","is_ca":true},{"name":"Dominique Lorrain","is_ca":true},{"name":"Lise R. Talbot","is_ca":true},{"name":"Francis Langlois","is_ca":true},{"name":"Hélène Pigot","is_ca":true},{"name":"Nathalie Bier","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1126356386984051,"gpt":0.3997860988166428,"spread":0.2871504601182376,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004796389,0.001455084,0.007307536,0.009116174,0.0005830647,0.002267536,0.001984832,0.001796682,0.004127785],"category_scores_gemma":[0.02962084,0.0008581189,0.006711259,0.009100749,0.0006906393,0.002085675,0.001169882,0.000882431,0.0003190752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003191332,"about_ca_system_score_gemma":0.009394032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01103353,"about_ca_topic_score_gemma":0.02972322,"domain_scores_codex":[0.995308,0.001278961,0.001951597,0.0003784684,0.0009350413,0.0001480034],"domain_scores_gemma":[0.9794009,0.01539639,0.003019144,0.000248649,0.00175185,0.0001830756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001355833,0.00001692195,0.0006720518,0.9638426,0.00483066,0.0000693355,0.0001457543,0.00005181531,0.00008408346,0.00008834532,0.0006831314,0.02937966],"study_design_scores_gemma":[0.0001459555,0.0002029814,0.00445341,0.9242169,0.05940484,0.000328678,0.0002423057,0.00008949865,0.0001675768,0.0001462597,0.01056694,0.00003481283],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001082674,0.9978148,0.0001341369,0.0001046251,0.00006457525,0.0002656959,0.0002961989,0.000007377388,0.0002298753],"genre_scores_gemma":[0.01229303,0.9859427,0.0005714118,0.0002922536,0.00005365856,0.0005329505,0.0002171887,0.00000395725,0.00009296346],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01103353,"threshold_uncertainty_score":0.02536601,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2803793592","doi":"10.1109/jbhi.2018.2839771","title":"Detecting Alzheimer's Disease on Small Dataset: A Knowledge Transfer Perspective","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":91,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research; National Institute of Biomedical Imaging and Bioengineering; Henan University of Science and Technology; National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; National Natural Science Foundation of China; U.S. Department of Defense","keywords":"Computer science; Perspective (graphical); Artificial intelligence; Machine learning; Sample size determination; Sample (material); Data sharing; Transfer of learning; Neuroimaging; CAD; Feature (linguistics); Data mining; Pattern recognition (psychology); Medicine; Statistics; Pathology","authors":[{"name":"Wei Li","is_ca":false},{"name":"Yifei Zhao","is_ca":false},{"name":"Xi Chen","is_ca":false},{"name":"Yang Xiao","is_ca":false},{"name":"Yuanyuan Qin","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.136331636976127,"gpt":0.3723395642373203,"spread":0.2360079272611934,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00469248,0.0009057308,0.0009804817,0.002074924,0.0006131568,0.001372569,0.001498629,0.001387324,0.0007280497],"category_scores_gemma":[0.01330603,0.0002275917,0.0007959506,0.001680724,0.0008383948,0.003115937,0.001772581,0.00127113,0.0002284026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009365987,"about_ca_system_score_gemma":0.0008384862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003967967,"about_ca_topic_score_gemma":0.00319771,"domain_scores_codex":[0.9978497,0.0008378305,0.0001356919,0.0006213694,0.0004077343,0.0001475913],"domain_scores_gemma":[0.9926322,0.005061837,0.0004163793,0.0009897607,0.0007416926,0.0001580952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007901767,0.001146173,0.03336435,0.0004343751,0.0005587871,0.0008177369,0.0005548658,0.2479971,0.01418971,0.01042342,0.006712685,0.6830107],"study_design_scores_gemma":[0.00002959689,0.0001993171,0.006989718,0.00002685123,0.0001048932,0.0001854717,0.0002269189,0.9678543,0.006267793,0.01625484,0.001832248,0.00002807203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2611759,0.002193084,0.7283077,0.002646229,0.0001850538,0.0003304789,0.0009163796,0.001010769,0.003234388],"genre_scores_gemma":[0.912923,0.0006589878,0.08346425,0.0003685646,0.0001999697,0.0001933908,0.001172413,0.00003850231,0.0009808707],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00469248,"threshold_uncertainty_score":0.02481651,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4317761548","doi":"10.1109/jbhi.2023.3239305","title":"Stress Detection Through Wrist-Based Electrodermal Activity Monitoring and Machine Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Mental Health Research Topics","field":"Psychology","cited_by":91,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; Huawei Technologies (Canada); University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Support vector machine; Machine learning; Computer science; Artificial intelligence; Stress (linguistics); Smartwatch; Wearable technology; Mental health; Feature extraction; Psychology; Embedded system; Psychiatry","authors":[{"name":"Petros Spachos","is_ca":true},{"name":"Pai Chet Ng","is_ca":true},{"name":"Yuanhao Yu","is_ca":true},{"name":"Yang Wang","is_ca":true},{"name":"Konstantinos N. Plataniotis","is_ca":true},{"name":"Dimitrios Hatzinakos","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1253246149996139,"gpt":0.4445201647737794,"spread":0.3191955497741655,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000390761,0.0006559294,0.0005352101,0.000971019,0.0001395641,0.0005857244,0.0003278943,0.0006336863,0.0007159301],"category_scores_gemma":[0.001146927,0.0001454554,0.0003744793,0.0008973577,0.0001735941,0.0005415073,0.0002691323,0.0002986917,0.0004883378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001078986,"about_ca_system_score_gemma":0.000103565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000583217,"about_ca_topic_score_gemma":0.000883465,"domain_scores_codex":[0.9996744,0.00007788293,0.00003070877,0.00009795033,0.00009270215,0.00002624948],"domain_scores_gemma":[0.9996529,0.0001355081,0.00008851003,0.00002899635,0.00007778164,0.00001626891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000452555,0.0003473169,0.0396272,0.0005513439,0.0002014039,0.0003195737,0.000169786,0.009035246,0.1109192,0.0004668464,0.001608499,0.836301],"study_design_scores_gemma":[0.00010181,0.002053324,0.257523,0.000312721,0.000339285,0.00249592,0.0004713022,0.6145924,0.1111339,0.00420363,0.006612214,0.0001605228],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4776221,0.007049971,0.5065008,0.0005384627,0.0003178256,0.0001936689,0.0009987311,0.001527211,0.005251259],"genre_scores_gemma":[0.9257729,0.002534029,0.06935662,0.0001303367,0.000171039,0.0001105419,0.0004565308,0.00002643484,0.001441445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000971019,"threshold_uncertainty_score":0.002395034,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4200044348","doi":"10.1109/jbhi.2021.3133103","title":"Aspect Based Twitter Sentiment Analysis on Vaccination and Vaccine Types in COVID-19 Pandemic With Deep Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":90,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Vaccination; Pandemic; Turkish; Coronavirus disease 2019 (COVID-19); Sentiment analysis; Social media; Medicine; Computer science; Family medicine; Artificial intelligence; Virology; World Wide Web; Infectious disease (medical specialty); Disease","authors":[{"name":"İrfan Aygün","is_ca":false},{"name":"Buket Kaya","is_ca":false},{"name":"Mehmet Kaya","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0398748612608444,"gpt":0.36289986281554,"spread":0.3230250015546957,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006354601,0.0007912231,0.0003882704,0.001595647,0.000335905,0.0006171871,0.0002759274,0.0005583916,0.001155647],"category_scores_gemma":[0.001602338,0.0001276138,0.0007312897,0.0008841247,0.0001512206,0.000801362,0.0005216218,0.0005754031,0.0009753676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005385726,"about_ca_system_score_gemma":0.0003521189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005483111,"about_ca_topic_score_gemma":0.01025921,"domain_scores_codex":[0.9996599,0.00009498596,0.00003890413,0.00006891474,0.00006730267,0.00007002764],"domain_scores_gemma":[0.9994617,0.0002080976,0.00008045303,0.00004512236,0.0001600706,0.00004458547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002513569,0.001352229,0.3598298,0.001137502,0.0005809835,0.001220792,0.001343821,0.03776838,0.03203568,0.001754541,0.05884145,0.5016212],"study_design_scores_gemma":[0.00007088025,0.0005532904,0.2260696,0.0001807207,0.0002649449,0.0003863916,0.002574117,0.7231597,0.01662383,0.002536027,0.02749294,0.00008756308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9408959,0.001425752,0.02233787,0.001661938,0.0005859242,0.0002576337,0.02032923,0.00138648,0.01111924],"genre_scores_gemma":[0.9541653,0.0005031347,0.01688227,0.0002707181,0.0001763134,0.0001337524,0.02346899,0.00004929876,0.004350191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005483111,"threshold_uncertainty_score":0.0109024,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2951467890","doi":"10.1109/jbhi.2019.2923209","title":"Vision-Based Freezing of Gait Detection With Anatomic Directed Graph Representation","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Australian Research Council; National Health and Medical Research Council; Parkinson Canada; University of Sydney","keywords":"Computer science; Artificial intelligence; Graph; Context (archaeology); Feature learning; Convolutional neural network; Deep learning; Machine learning; Computer vision; Pattern recognition (psychology)","authors":[{"name":"Kun Hu","is_ca":false},{"name":"Zhiyong Wang","is_ca":false},{"name":"Shaohui Mei","is_ca":false},{"name":"Kaylena A. Ehgoetz Martens","is_ca":false},{"name":"Tingting Yao","is_ca":false},{"name":"Simon J.G. Lewis","is_ca":false},{"name":"Dagan Feng","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01137344938942956,"gpt":0.2653487971855819,"spread":0.2539753477961523,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002619417,0.0007164163,0.0004836853,0.001658016,0.0001840088,0.0004589947,0.0008021875,0.0005286214,0.0006566519],"category_scores_gemma":[0.000966356,0.000225331,0.0007237645,0.0009819748,0.000280536,0.0005928085,0.0005584642,0.0004801347,0.0002680682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005103382,"about_ca_system_score_gemma":0.0005141726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009103579,"about_ca_topic_score_gemma":0.01005004,"domain_scores_codex":[0.9998019,0.00004203464,0.0000119935,0.00007212869,0.00004557502,0.00002640295],"domain_scores_gemma":[0.9997491,0.00008046289,0.00004871327,0.000033067,0.00006839068,0.00002024576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003932585,0.0002592756,0.006384366,0.0001956919,0.0001916803,0.000362228,0.0001181744,0.3028156,0.04310638,0.003721934,0.004916695,0.6375347],"study_design_scores_gemma":[0.000005084192,0.00003300209,0.001283316,0.000006097738,0.00001885099,0.00009790195,0.00001363977,0.993098,0.003333607,0.001749602,0.00035298,0.000007956478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06853695,0.00039175,0.9279084,0.0001291458,0.00004181492,0.0001054159,0.00038555,0.001601815,0.0008991004],"genre_scores_gemma":[0.7548385,0.0004493018,0.241208,0.0001357744,0.00004583205,0.0001058365,0.001494795,0.00008489304,0.001637158],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009103579,"threshold_uncertainty_score":0.01810116,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1990956587","doi":"10.1109/jbhi.2014.2360156","title":"Automatic Annotation of Seismocardiogram With High-Frequency Precordial Accelerations","year":2014,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":86,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"Computer science; Envelope (radar); Computer-supported cooperative work; Accelerometer; Precordial examination; Waveform; Hilbert transform; Annotation; SIGNAL (programming language); Algorithm; Pattern recognition (psychology); Artificial intelligence; Electrocardiography; Speech recognition; Computer vision; Work (physics); Physics; Radar; Medicine; Telecommunications; Cardiology","authors":[{"name":"Farzad Khosrow-Khavar","is_ca":true},{"name":"Kouhyar Tavakolian","is_ca":false},{"name":"Andrew P. Blaber","is_ca":true},{"name":"John M. Zanetti","is_ca":false},{"name":"Reza Fazel-Rezai","is_ca":false},{"name":"Carlo Menon","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01941527698704807,"gpt":0.3008379881598308,"spread":0.2814227111727827,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000913327,0.001062181,0.0007931953,0.003648832,0.0002772168,0.0009374553,0.000725381,0.0009529681,0.001443967],"category_scores_gemma":[0.003229624,0.0001981876,0.0005373913,0.001361834,0.0002201137,0.0005214024,0.0007702297,0.000471324,0.001435884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001511778,"about_ca_system_score_gemma":0.0004966088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001480635,"about_ca_topic_score_gemma":0.00210636,"domain_scores_codex":[0.9992024,0.0001553154,0.0001038255,0.0002068877,0.0002511014,0.00008038645],"domain_scores_gemma":[0.9983839,0.000445274,0.0002339235,0.0002490715,0.0005894254,0.00009856639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001632302,0.0003159257,0.03172133,0.0006466602,0.0001301568,0.001093108,0.0003351195,0.00715281,0.1764659,0.001385247,0.01093179,0.7681897],"study_design_scores_gemma":[0.0002551937,0.0006159049,0.2539104,0.0002111899,0.0002825338,0.003468929,0.0006360013,0.5796779,0.1301425,0.005359359,0.02525822,0.0001819658],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2841037,0.001677705,0.6991172,0.0002385183,0.0003391584,0.0004239322,0.003882823,0.007260619,0.002956372],"genre_scores_gemma":[0.6255605,0.0009701522,0.3573972,0.0001227379,0.0003930207,0.0003449813,0.01149049,0.0003572191,0.00336377],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003648832,"threshold_uncertainty_score":0.00483048,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2968097744","doi":"10.1109/jbhi.2019.2934477","title":"Direct Cup-to-Disc Ratio Estimation for Glaucoma Screening via Semi-Supervised Learning","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":86,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; National Natural Science Foundation of China","keywords":"Glaucoma; Optic disc; Optic cup (embryology); Artificial intelligence; Computer science; Segmentation; Convolutional neural network; Optic disk; Pattern recognition (psychology); Robustness (evolution); Feature extraction; Deep learning; Supervised learning; Feature (linguistics); Artificial neural network; Ophthalmology; Medicine","authors":[{"name":"Rongchang Zhao","is_ca":false},{"name":"Xuanlin Chen","is_ca":false},{"name":"Xiyao Liu","is_ca":false},{"name":"Zailiang Chen","is_ca":false},{"name":"Fan Guo","is_ca":false},{"name":"Shuo Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02525178807350699,"gpt":0.3356276650895234,"spread":0.3103758770160164,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009592034,0.001047893,0.0009845434,0.001185024,0.0003516601,0.0006129526,0.00127568,0.0009082093,0.001124791],"category_scores_gemma":[0.002869608,0.0003502298,0.0008883152,0.0005216713,0.0004411604,0.0006293787,0.0007549605,0.0008461552,0.0008669135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005372548,"about_ca_system_score_gemma":0.001028076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005206401,"about_ca_topic_score_gemma":0.00874297,"domain_scores_codex":[0.9992123,0.000145056,0.00006481016,0.000272924,0.000223717,0.00008124311],"domain_scores_gemma":[0.9986103,0.0004326919,0.0002353973,0.0002111877,0.0004321929,0.00007824468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007059238,0.0007076331,0.01780818,0.0004191492,0.0003197898,0.0003565332,0.0001302311,0.107689,0.04036153,0.001022596,0.0121957,0.8182837],"study_design_scores_gemma":[0.00003604765,0.0001202619,0.004582272,0.00002793922,0.00004952111,0.0002344647,0.00002203637,0.9817964,0.01103998,0.0009527755,0.001112614,0.00002574841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2525246,0.003795587,0.7241571,0.0005628969,0.000219529,0.0003389855,0.001359867,0.01230052,0.004740834],"genre_scores_gemma":[0.8316161,0.0005710652,0.1607374,0.0003664006,0.0001619244,0.0001691764,0.003005042,0.0002016021,0.003171344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005206401,"threshold_uncertainty_score":0.01035219,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2070952583","doi":"10.1109/jbhi.2014.2301156","title":"Development of mHealth Applications for Pre-Eclampsia Triage","year":2014,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Neonatal Respiratory Health Research","field":"Medicine","cited_by":86,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"University of British Columbia; Grand Challenges Canada; Bill and Melinda Gates Foundation","keywords":"mHealth; Triage; Medicine; Medical emergency; Eclampsia; Mobile phone; Telemedicine; Referral; Health care; Computer science; Pregnancy; Family medicine; Nursing; Telecommunications; Psychological intervention","authors":[{"name":"Dustin Dunsmuir","is_ca":true},{"name":"Beth A. Payne","is_ca":true},{"name":"Garth Cloete","is_ca":false},{"name":"Christian L. Petersen","is_ca":true},{"name":"Matthias Görges","is_ca":true},{"name":"Joanne Lim","is_ca":true},{"name":"Peter von Dadelszen","is_ca":true},{"name":"Guy A. Dumont","is_ca":true},{"name":"J. Mark Ansermino","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09951777413278212,"gpt":0.4520898469881459,"spread":0.3525720728553637,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002525528,0.0007696464,0.0004828088,0.0009529037,0.0004356659,0.001529488,0.001225227,0.0009057239,0.00358325],"category_scores_gemma":[0.006335483,0.00037356,0.0005728337,0.0004108535,0.000198434,0.001457106,0.0008472323,0.001077048,0.002444931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003202308,"about_ca_system_score_gemma":0.0009890415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009452114,"about_ca_topic_score_gemma":0.0009504003,"domain_scores_codex":[0.9988301,0.0002235947,0.0001458871,0.0001249713,0.0005732536,0.0001022292],"domain_scores_gemma":[0.9963206,0.001237111,0.0001591645,0.0002220533,0.001812681,0.0002483608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009268008,0.000877818,0.007475791,0.001463549,0.00009698346,0.002087303,0.001138468,0.002230982,0.06982099,0.002354777,0.02653611,0.8849904],"study_design_scores_gemma":[0.0009590636,0.006604935,0.05016415,0.002425792,0.0005610247,0.006032602,0.001995737,0.1138112,0.3124429,0.006246534,0.4982495,0.0005065005],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1742393,0.00424719,0.6993278,0.003366904,0.00237941,0.0140867,0.004000925,0.06919798,0.02915381],"genre_scores_gemma":[0.1999879,0.002577989,0.763742,0.001518164,0.0003371151,0.003598263,0.004446315,0.001504725,0.02228764],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00358325,"threshold_uncertainty_score":0.01335639,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1988782368","doi":"10.1109/jbhi.2014.2377517","title":"A Predictive Model for Personalized Therapeutic Interventions in Non-small Cell Lung Cancer","year":2014,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Lung Cancer Treatments and Mutations","field":"Medicine","cited_by":85,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"Canadian Institutes of Health Research","keywords":"Lung cancer; Medicine; Oncology; Epidermal growth factor receptor; Decision tree; Erlotinib; Targeted therapy; Internal medicine; Personalized medicine; non-small cell lung cancer (NSCLC); Clinical trial; Clinical decision support system; Cancer; Bioinformatics; Decision support system; Machine learning; Computer science; Artificial intelligence; Biology","authors":[{"name":"Nelofar Kureshi","is_ca":true},{"name":"Syed Sibte Raza Abidi","is_ca":true},{"name":"Christian Blouin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05089780953981815,"gpt":0.4030895795922225,"spread":0.3521917700524044,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001882669,0.0006427296,0.001045445,0.001019238,0.0003971682,0.00114066,0.0007335005,0.0008712652,0.002005678],"category_scores_gemma":[0.005085754,0.0002837786,0.0008074893,0.0007511586,0.0002582816,0.0007252493,0.0004106839,0.001037088,0.0003825613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008132209,"about_ca_system_score_gemma":0.001239097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009877266,"about_ca_topic_score_gemma":0.006002546,"domain_scores_codex":[0.9994879,0.0001944968,0.00004535245,0.00009448711,0.00009733837,0.0000803776],"domain_scores_gemma":[0.9977437,0.001787425,0.000127998,0.00004548745,0.0002391917,0.00005619227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002588475,0.000127169,0.006126015,0.00007624071,0.00007945477,0.0002257648,0.00004909325,0.9182594,0.0005750532,0.002247032,0.002172008,0.06980389],"study_design_scores_gemma":[0.000006113383,0.00002381715,0.000360798,0.000006425428,0.00001257582,0.00001532174,0.000005456774,0.9981322,0.00009284494,0.001206171,0.0001350107,0.000003316595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2924891,0.002245103,0.69378,0.003138056,0.0003600423,0.0002451926,0.001870488,0.002039497,0.003832557],"genre_scores_gemma":[0.9682141,0.0004072606,0.02910859,0.0001319173,0.00008593195,0.0001313065,0.0007275693,0.00002143905,0.001171785],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009877266,"threshold_uncertainty_score":0.01963955,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3014082018","doi":"10.1109/jbhi.2019.2958389","title":"Predicting Human lncRNA-Disease Associations Based on Geometric Matrix Completion","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":76,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Higher Education Discipline Innovation Project; Hunan Provincial Science and Technology Department; National Natural Science Foundation of China","keywords":"Computer science; Semantic similarity; Disease; Robustness (evolution); Data mining; Similarity (geometry); Matrix completion; Artificial intelligence; Gaussian; Theoretical computer science; Pattern recognition (psychology); Machine learning; Algorithm; Medicine; Biology; Genetics","authors":[{"name":"Chengqian Lu","is_ca":false},{"name":"Mengyun Yang","is_ca":false},{"name":"Min Li","is_ca":false},{"name":"Yaohang Li","is_ca":false},{"name":"Fang‐Xiang Wu","is_ca":true},{"name":"Jianxin Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04527594550514697,"gpt":0.3581509237411948,"spread":0.3128749782360478,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009612408,0.0009053939,0.0009174856,0.001696561,0.0004426251,0.0008593996,0.001052948,0.0007771505,0.00167966],"category_scores_gemma":[0.004429194,0.0003590226,0.001353831,0.001166448,0.0007205739,0.0009086589,0.001236095,0.001187662,0.0007364036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005674152,"about_ca_system_score_gemma":0.001296459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006552407,"about_ca_topic_score_gemma":0.008183779,"domain_scores_codex":[0.9990965,0.0002099722,0.00005489109,0.0003706974,0.0001917477,0.00007618337],"domain_scores_gemma":[0.997736,0.00128876,0.0003201172,0.0002225947,0.0002762795,0.0001563065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005301634,0.0002671925,0.05086213,0.000385376,0.0003911949,0.0007613406,0.0003024271,0.6088743,0.01992155,0.01568189,0.007131962,0.2948905],"study_design_scores_gemma":[0.000009995694,0.00004077961,0.001891342,0.000008189787,0.00001760789,0.0001302719,0.0000253669,0.9877665,0.001279752,0.007820069,0.0009942047,0.00001583193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07603574,0.0008744877,0.9197918,0.0003520892,0.0000426546,0.00008065125,0.0008700775,0.001054092,0.000898384],"genre_scores_gemma":[0.691717,0.0008331505,0.2995228,0.000251361,0.0001369465,0.0002068576,0.004391125,0.0001202889,0.002820486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006552407,"threshold_uncertainty_score":0.0130285,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4366082712","doi":"10.1109/jbhi.2023.3267857","title":"Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&amp;Ms Challenge","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":72,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Canadian VIGOUR Centre; University of Alberta; Circle Cardiovascular Imaging","funders":"Fundação para a Ciência e a Tecnologia; Horizon 2020 Framework Programme; Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona; Ministerio de Economía y Competitividad; Institució Catalana de Recerca i Estudis Avançats; Ministerio de Ciencia, Innovación y Universidades","keywords":"Segmentation; Ventricle; Artificial intelligence; Deep learning; Medicine; Computer science; Cardiac Ventricle; Cardiology","authors":[{"name":"Carlos Martín-Isla","is_ca":false},{"name":"Víctor M. Campello","is_ca":false},{"name":"Cristian Izquierdo","is_ca":false},{"name":"Kaisar Kushibar","is_ca":false},{"name":"Carla Sendra-Balcells","is_ca":false},{"name":"Polyxeni Gkontra","is_ca":false},{"name":"Alireza Sojoudi","is_ca":true},{"name":"Mitchell J. Fulton","is_ca":false},{"name":"Tewodros Weldebirhan Arega","is_ca":false},{"name":"Kumaradevan Punithakumar","is_ca":true},{"name":"Lei Li","is_ca":false},{"name":"Xiaowu Sun","is_ca":false},{"name":"Yasmina Al Khalil","is_ca":false},{"name":"Di Liu","is_ca":false},{"name":"Sana Jabbar","is_ca":false},{"name":"Sandro Queirós","is_ca":false},{"name":"Francesco Galati","is_ca":false},{"name":"Moona Mazher","is_ca":false},{"name":"Zheyao Gao","is_ca":false},{"name":"Marcel Beetz","is_ca":false},{"name":"Lennart Tautz","is_ca":false},{"name":"Christoforos Galazis","is_ca":false},{"name":"Marta Varela","is_ca":false},{"name":"Markus Hüllebrand","is_ca":false},{"name":"Vicente Grau","is_ca":false},{"name":"Xiahai Zhuang","is_ca":false},{"name":"Domènec Puig","is_ca":false},{"name":"María A. Zuluaga","is_ca":false},{"name":"Hassan Mohy‐ud‐Din","is_ca":false},{"name":"Dimitris Metaxas","is_ca":false},{"name":"Marcel Breeuwer","is_ca":false},{"name":"Rob J. van der Geest","is_ca":false},{"name":"Michelle Noga","is_ca":true},{"name":"Stéphanie Bricq","is_ca":false},{"name":"Mark E. Rentschler","is_ca":false},{"name":"Andrea Guala","is_ca":false},{"name":"Steffen E. Petersen","is_ca":false},{"name":"Sérgio Escalera","is_ca":false},{"name":"José F. Rodríguez‐Palomares","is_ca":false},{"name":"Karim Lekadir","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02931649168218435,"gpt":0.3687435860291679,"spread":0.3394270943469835,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003984468,0.00158323,0.001870921,0.001780523,0.0005810869,0.001839528,0.001959423,0.004479413,0.001313334],"category_scores_gemma":[0.007833214,0.0007410983,0.001350044,0.001360844,0.001000314,0.001409931,0.001584917,0.002373213,0.0012936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001162547,"about_ca_system_score_gemma":0.0016789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006371822,"about_ca_topic_score_gemma":0.007978743,"domain_scores_codex":[0.9974718,0.0006623465,0.0002214921,0.0007752146,0.0006868335,0.0001823219],"domain_scores_gemma":[0.9959579,0.002507392,0.0002481688,0.0003521201,0.0006446213,0.0002898243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005336248,0.0002035426,0.004533424,0.001010571,0.0002290107,0.0006436597,0.0002965948,0.1078782,0.01544239,0.00580526,0.06552523,0.7978984],"study_design_scores_gemma":[0.00009830231,0.0001732388,0.00579558,0.0002682876,0.00009678326,0.001740746,0.0003146279,0.9146084,0.01702555,0.02516352,0.03462714,0.00008783748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1938493,0.05533022,0.6918113,0.03531935,0.001691925,0.0003743164,0.007087345,0.007951329,0.006585017],"genre_scores_gemma":[0.4221422,0.01904228,0.5218409,0.005325244,0.002740814,0.0003127753,0.01901938,0.001498986,0.008077336],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006371822,"threshold_uncertainty_score":0.02107215,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4283688663","doi":"10.1109/jbhi.2022.3187037","title":"Blockchain-Based Privacy Preservation Scheme for Misbehavior Detection in Lightweight IoMT Devices","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Research Foundation of Korea; Queen's University; National Research Foundation; Queen's University Belfast","keywords":"Blockchain; Computer science; Scheme (mathematics); Computer security; Computer network; Information privacy; Internet privacy; Cryptography","authors":[{"name":"Sandi Rahmadika","is_ca":false},{"name":"Philip Virgil Astillo","is_ca":false},{"name":"Gaurav Choudhary","is_ca":false},{"name":"Daniel Gerbi Duguma","is_ca":false},{"name":"Vishal Sharma","is_ca":false},{"name":"Ilsun You","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03177167625877878,"gpt":0.303978789806211,"spread":0.2722071135474322,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001721038,0.00048379,0.0008502124,0.0007577477,0.0009397048,0.001152994,0.001896906,0.00107476,0.002404366],"category_scores_gemma":[0.005546397,0.0002290933,0.000519419,0.0007005846,0.0009173043,0.002965736,0.002048164,0.0009311886,0.0004676433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00111533,"about_ca_system_score_gemma":0.002648249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002898731,"about_ca_topic_score_gemma":0.003194355,"domain_scores_codex":[0.9983414,0.0003890746,0.000156924,0.000363831,0.0004966277,0.0002520605],"domain_scores_gemma":[0.9969067,0.001073009,0.0004627373,0.0007607481,0.0006157841,0.0001809504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009543181,0.000306437,0.009759103,0.0002548605,0.0001135683,0.0008613865,0.0005791956,0.6276779,0.01853851,0.07032893,0.003083066,0.2675427],"study_design_scores_gemma":[0.00002740884,0.00008474154,0.000332209,0.00001297758,0.00002316133,0.0001759622,0.000028742,0.9752001,0.004717506,0.01840593,0.0009754925,0.00001574232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08650289,0.0002368335,0.9074296,0.0004913725,0.00004664641,0.0002037507,0.0002597501,0.001119502,0.00370973],"genre_scores_gemma":[0.9750764,0.0001105366,0.02238612,0.00006225597,0.00001690505,0.0000801299,0.0001554187,0.00001585674,0.002096415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002898731,"threshold_uncertainty_score":0.009101808,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2367937674","doi":"10.1109/jbhi.2016.2567298","title":"Noncontact Vision-Based Cardiopulmonary Monitoring in Different Sleeping Positions","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University Health Network; Toronto Rehabilitation Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Toronto Rehabilitation Institute","keywords":"Computer vision; Computer science; Artificial intelligence; Remote patient monitoring; Cardiopulmonary resuscitation; Medicine; Emergency medicine; Radiology","authors":[{"name":"Michael H. Li","is_ca":true},{"name":"Azadeh Yadollahi","is_ca":true},{"name":"Babak Taati","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01977004540833956,"gpt":0.2832428654325244,"spread":0.2634728200241849,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002836388,0.0002792953,0.0002348338,0.0003611491,0.0001138459,0.0002523739,0.0003348764,0.0004010052,0.0009344158],"category_scores_gemma":[0.0009120891,0.0001214573,0.0001452419,0.000151201,0.000166666,0.0002518698,0.0003958666,0.000134314,0.0002211556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001086892,"about_ca_system_score_gemma":0.0001496745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000579367,"about_ca_topic_score_gemma":0.001399018,"domain_scores_codex":[0.9996934,0.00008843168,0.00001217975,0.00008260903,0.0001012468,0.00002208942],"domain_scores_gemma":[0.9997448,0.000111884,0.00003944701,0.00002629485,0.00005591264,0.00002162734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001421927,0.0004114102,0.01689801,0.0002879399,0.00006000269,0.0003130366,0.0003797322,0.002833551,0.7976678,0.0001809488,0.0004128202,0.1791328],"study_design_scores_gemma":[0.000407896,0.0096008,0.4489371,0.00007132014,0.0003007348,0.004442386,0.0005363254,0.1610566,0.3717395,0.0006669043,0.002122484,0.0001177347],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9322842,0.0003150634,0.06577193,0.00004481935,0.00002963012,0.00009719584,0.0001035552,0.0002065831,0.001146897],"genre_scores_gemma":[0.9746979,0.0001378248,0.02418433,0.00004308957,0.00001654487,0.00005947274,0.00006963386,0.00001557865,0.000775744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009344158,"threshold_uncertainty_score":0.003125966,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2947659587","doi":"10.1109/jbhi.2019.2920356","title":"Separation of Fetal-ECG From Single-Channel Abdominal ECG Using Activation Scaled Non-Negative Matrix Factorization","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Non-negative matrix factorization; Pattern recognition (psychology); Computer science; Artificial intelligence; Blind signal separation; Matrix decomposition; Electrocardiography; SIGNAL (programming language); Fetus; Independent component analysis; Channel (broadcasting); Medicine; Cardiology; Pregnancy","authors":[{"name":"Dharmendra Gurve","is_ca":true},{"name":"Sridhar Krishnan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04086625933964426,"gpt":0.3411678111525153,"spread":0.300301551812871,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006248733,0.001144956,0.0006823325,0.0006712974,0.0002370634,0.0004287319,0.0004868333,0.0008144221,0.0009731497],"category_scores_gemma":[0.002196619,0.0001942911,0.001099888,0.0005958689,0.0003112564,0.0005561655,0.0004638417,0.0009974218,0.0008288824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002540656,"about_ca_system_score_gemma":0.0006182186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003154631,"about_ca_topic_score_gemma":0.003751348,"domain_scores_codex":[0.9994915,0.0000992826,0.00003672325,0.0001445923,0.0001788481,0.0000491164],"domain_scores_gemma":[0.999466,0.0002236268,0.00006713424,0.00006331713,0.0001479802,0.00003204645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006615276,0.0002061472,0.002756213,0.0004912476,0.0001738598,0.0009023959,0.0001903138,0.07530013,0.1633894,0.002995461,0.01016111,0.7427723],"study_design_scores_gemma":[0.00006221136,0.0002145621,0.006514315,0.00005072877,0.00006533624,0.001212262,0.00009933528,0.9404473,0.03944229,0.003741458,0.008094997,0.00005515648],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02092187,0.0007310184,0.9762738,0.0002283008,0.0001148512,0.00007624444,0.0002774942,0.0006262752,0.0007502949],"genre_scores_gemma":[0.2136938,0.001327999,0.777617,0.0002415627,0.0002180433,0.0001737208,0.003123379,0.0001782927,0.003426185],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003154631,"threshold_uncertainty_score":0.006272554,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2083125248","doi":"10.1109/jbhi.2013.2277837","title":"Toward Automatic Mitotic Cell Detection and Segmentation in Multispectral Histopathological Images","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"AI in cancer detection","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Multispectral image; Computer science; Pattern recognition (psychology); Segmentation; Discriminative model; Linear discriminant analysis; Feature extraction; Computer vision; Image segmentation; Mitosis; Biology","authors":[{"name":"Cheng Lu","is_ca":true},{"name":"Mrinal Mandal","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0257957948046617,"gpt":0.2816623811225924,"spread":0.2558665863179307,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001092379,0.0005735253,0.0006028673,0.003406215,0.0003441536,0.0009245251,0.0009012823,0.0008335563,0.0006760912],"category_scores_gemma":[0.001783332,0.0003356303,0.0005056913,0.001184575,0.0004086722,0.0007974435,0.0005741103,0.0005455667,0.0009371538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003390719,"about_ca_system_score_gemma":0.0005754997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001338928,"about_ca_topic_score_gemma":0.002664447,"domain_scores_codex":[0.9992985,0.0001323635,0.00003891503,0.0001811268,0.0002767547,0.00007240795],"domain_scores_gemma":[0.9988363,0.0002867107,0.0002331822,0.0001998449,0.0003849309,0.00005897592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002998072,0.0001947305,0.009141005,0.0001884089,0.0000439496,0.0001677634,0.0001514749,0.01029869,0.3845891,0.00155765,0.001949477,0.591418],"study_design_scores_gemma":[0.00004084568,0.0002523164,0.03045237,0.00003693952,0.00008899644,0.001196193,0.0002232155,0.6820837,0.2764227,0.002688071,0.006465365,0.00004934246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1382191,0.0006707493,0.8575388,0.0001751318,0.00003314386,0.00009682107,0.0001502164,0.001995444,0.001120577],"genre_scores_gemma":[0.2934554,0.0004695121,0.7039712,0.00008643958,0.00004779387,0.00006493281,0.0005168422,0.00009714513,0.001290705],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003406215,"threshold_uncertainty_score":0.005777121,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2899377266","doi":"10.1109/jbhi.2018.2878907","title":"Effects of Confidence-Based Rejection on Usability and Error in Pattern Recognition-Based Myoelectric Control","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Usability; Computer science; Error detection and correction; Word error rate; Type I and type II errors; Mistake; Classifier (UML); Support vector machine; Artificial intelligence; Speech recognition; Pattern recognition (psychology); Machine learning; Statistics; Human–computer interaction; Algorithm; Mathematics","authors":[{"name":"Jason W. Robertson","is_ca":true},{"name":"Kevin Englehart","is_ca":true},{"name":"Erik Scheme","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01934719538976797,"gpt":0.2673950860805104,"spread":0.2480478906907424,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004850814,0.0007021236,0.0004749511,0.0005407392,0.0001641393,0.001044201,0.0003540556,0.0008164202,0.001187492],"category_scores_gemma":[0.08148049,0.0002703335,0.0003610835,0.0002403521,0.0006404354,0.0009426789,0.0007706665,0.000606273,0.0002063924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001840204,"about_ca_system_score_gemma":0.0001459044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005275473,"about_ca_topic_score_gemma":0.0004612819,"domain_scores_codex":[0.9953734,0.002013705,0.0005807223,0.0004881573,0.001272127,0.0002718749],"domain_scores_gemma":[0.873064,0.1083531,0.008487177,0.003803781,0.004602119,0.00168978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.04155046,0.005514812,0.406465,0.001057171,0.0009059086,0.001033171,0.00538322,0.01794081,0.2338746,0.00049976,0.0007023416,0.2850727],"study_design_scores_gemma":[0.0003953852,0.03159872,0.8961091,0.0001089967,0.0003987577,0.001028573,0.0009474293,0.03106157,0.03704893,0.0007145531,0.0003936389,0.0001942482],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960134,0.000128093,0.003178217,0.00002440793,0.00001315161,0.00002239854,0.00001261085,0.00004620844,0.0005615324],"genre_scores_gemma":[0.9985714,0.00002853137,0.001145153,0.00001755262,0.000008284381,0.00001335119,0.00002219143,0.00001766468,0.0001757939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004850814,"threshold_uncertainty_score":0.0256539,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2083354351","doi":"10.1109/jbhi.2014.2336757","title":"A Decision-Support Framework for Promoting Independent Living and Ageing Well","year":2014,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Seventh Framework Programme; European Commission; AGE-WELL","keywords":"Computer science; Inference; Schema (genetic algorithms); Artificial intelligence; Decision support system; Independent living; Quality of life (healthcare); Cognition; Fuzzy cognitive map; Gerontology; Psychology; Machine learning; Fuzzy logic; Medicine; Fuzzy set; Psychiatry; Nursing","authors":[{"name":"Antonis Billis","is_ca":false},{"name":"Elpiniki I. Papageorgiou","is_ca":false},{"name":"Christos A. Frantzidιs","is_ca":false},{"name":"Marianna Tsatali","is_ca":false},{"name":"Anthoula Tsolaki","is_ca":false},{"name":"Panagiotis D. Bamidis","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02961315977953147,"gpt":0.3268836425579978,"spread":0.2972704827784663,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002036674,0.0007682951,0.0005804257,0.000804453,0.0009390586,0.003100422,0.00166021,0.001411808,0.005417516],"category_scores_gemma":[0.002378967,0.000281865,0.0009924987,0.0006369028,0.000953341,0.002200034,0.001950689,0.001220648,0.0008421105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001351764,"about_ca_system_score_gemma":0.002614833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007840488,"about_ca_topic_score_gemma":0.008437963,"domain_scores_codex":[0.9990619,0.0003108498,0.00009543465,0.0002061031,0.0002319612,0.00009373517],"domain_scores_gemma":[0.9992664,0.0002827028,0.00006149224,0.00005619897,0.0001979474,0.000135233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001384969,0.0002895412,0.001189373,0.0002411357,0.00009436711,0.0007042606,0.000589858,0.3110079,0.003440001,0.5738281,0.005696628,0.1027804],"study_design_scores_gemma":[0.00004653708,0.00009252498,0.0002425013,0.0000788082,0.00005630123,0.0001221474,0.0001928237,0.7897412,0.00121822,0.1854335,0.02274173,0.00003367772],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00903096,0.0003952062,0.9765561,0.001693671,0.000115234,0.0001617376,0.0002397586,0.0005323631,0.01127492],"genre_scores_gemma":[0.4025239,0.0006622539,0.5855758,0.0003673072,0.0001191159,0.0004159087,0.0005197523,0.00004731349,0.009768619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007840488,"threshold_uncertainty_score":0.01812345,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2089459993","doi":"10.1109/titb.2012.2199595","title":"Automated Segmentation of the Melanocytes in Skin Histopathological Images","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"AI in cancer detection","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Image segmentation; Artificial intelligence; Segmentation; Computer science; Computer vision; Pattern recognition (psychology)","authors":[{"name":"Cheng Lu","is_ca":true},{"name":"Muhammad Habib Mahmood","is_ca":true},{"name":"Naresh Jha","is_ca":true},{"name":"Mrinal Mandal","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02082102809643108,"gpt":0.2961571124579657,"spread":0.2753360843615346,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004673681,0.0002791641,0.0003287027,0.001858798,0.0002277526,0.0005814226,0.0003286018,0.0004734207,0.0006285548],"category_scores_gemma":[0.0007809191,0.0002497904,0.0002996337,0.0005405049,0.0002872849,0.0004184948,0.0003508931,0.0002269914,0.0004136435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002057318,"about_ca_system_score_gemma":0.0003080971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009794757,"about_ca_topic_score_gemma":0.001694756,"domain_scores_codex":[0.9997105,0.00006963201,0.00001841942,0.00005750618,0.0001051619,0.00003873823],"domain_scores_gemma":[0.9996061,0.0001218163,0.00005352372,0.00006650002,0.0001292968,0.00002283393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002214002,0.00007330398,0.004810853,0.0001705754,0.00004282549,0.0003567591,0.0001596596,0.009117317,0.7434682,0.0008739679,0.0006195251,0.2400857],"study_design_scores_gemma":[0.00004569894,0.000292497,0.05601368,0.00005431336,0.00006935293,0.002707335,0.0004918003,0.4449731,0.4853395,0.002118482,0.007810025,0.00008407678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3443736,0.001106253,0.6514995,0.00009456542,0.00003585029,0.0001494073,0.0001280728,0.001194952,0.001417831],"genre_scores_gemma":[0.6172801,0.0007371416,0.3800289,0.00004626901,0.00002187941,0.00004630159,0.0002156628,0.000103071,0.001520755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001858798,"threshold_uncertainty_score":0.002471745,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2896337276","doi":"10.1109/jbhi.2018.2875456","title":"Dynamic Graph Theoretical Analysis of Functional Connectivity in Parkinson's Disease: The Importance of Fiedler Value","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":60,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Pacific Centre for Reproductive Medicine; University of British Columbia","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Dynamic functional connectivity; Graph theory; Connectomics; Power graph analysis; Modularity (biology); Functional magnetic resonance imaging; Graph; Computer science; Connectome; Functional connectivity; Artificial intelligence; Mathematics; Neuroscience; Psychology; Theoretical computer science; Biology; Combinatorics","authors":[{"name":"Jiayue Cai","is_ca":true},{"name":"Aiping Liu","is_ca":false},{"name":"Taomian Mi","is_ca":false},{"name":"Saurabh Garg","is_ca":false},{"name":"Wade Trappe","is_ca":false},{"name":"Martin J. McKeown","is_ca":true},{"name":"Z. Jane Wang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03348454030728518,"gpt":0.312632211544642,"spread":0.2791476712373568,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001184265,0.0003761614,0.0004911852,0.002705579,0.0003518919,0.0008341594,0.0004310692,0.0005466294,0.0006577098],"category_scores_gemma":[0.01009831,0.000165863,0.0004268574,0.001268797,0.001110536,0.001888809,0.0003982634,0.000633997,0.0001062148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006947542,"about_ca_system_score_gemma":0.0003733193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003492517,"about_ca_topic_score_gemma":0.002532854,"domain_scores_codex":[0.9996423,0.0001242108,0.00002162607,0.0001003329,0.00008131235,0.00003034927],"domain_scores_gemma":[0.9963061,0.002708563,0.0004202797,0.0002175875,0.0002475933,0.00009981908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003062235,0.0001305032,0.08218403,0.0003597475,0.0003632545,0.001097601,0.001103341,0.4972467,0.02064727,0.1509895,0.002592528,0.2429794],"study_design_scores_gemma":[0.000009313551,0.00009126598,0.04939698,0.00004471319,0.00004291987,0.0005094652,0.0001379491,0.8161386,0.001683245,0.13026,0.00160618,0.00007929122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5170943,0.00239777,0.4754713,0.0007922378,0.00004985967,0.0000696194,0.0004429963,0.0002393121,0.00344258],"genre_scores_gemma":[0.9642794,0.0007416552,0.03404271,0.00004759555,0.00004226277,0.00004634956,0.000285527,0.0000323157,0.0004821713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003492517,"threshold_uncertainty_score":0.006944358,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1554242441","doi":"10.1109/jbhi.2015.2445783","title":"Design and Evaluation of an Intelligent Remote Tidal Volume Variability Monitoring System in E-Health Applications","year":2015,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Spirometer; Wearable computer; Computer science; Bluetooth; Tidal volume; Respiratory monitoring; Remote patient monitoring; Accelerometer; ALARM; Breathing; Operability; Real-time computing; Simulation; Medicine; Spirometry; Engineering; Embedded system; Telecommunications; Respiratory system; Wireless; Internal medicine","authors":[{"name":"Atena Roshan Fekr","is_ca":true},{"name":"Katarzyna Radecka","is_ca":true},{"name":"Željko Žilić","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08896913081299526,"gpt":0.3450493120624503,"spread":0.2560801812494551,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007600229,0.0004412521,0.0005373248,0.0003147085,0.0002414542,0.000533236,0.001020855,0.0007277175,0.00230758],"category_scores_gemma":[0.0008277352,0.0001786697,0.0002733425,0.0001534937,0.0001884088,0.000508042,0.0003397094,0.0002281392,0.0006713389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000315578,"about_ca_system_score_gemma":0.0004596637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008888647,"about_ca_topic_score_gemma":0.0005841611,"domain_scores_codex":[0.9995447,0.0001205959,0.00004222307,0.0001088762,0.00013219,0.00005151862],"domain_scores_gemma":[0.9996104,0.00007079002,0.00003328745,0.00004179321,0.0001900797,0.00005371879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003340538,0.00230345,0.0248193,0.001257498,0.0002561745,0.001238722,0.0005260573,0.03706085,0.5848668,0.002281639,0.004746573,0.3373024],"study_design_scores_gemma":[0.0007337868,0.01368755,0.06265979,0.0001136634,0.0004747362,0.001439492,0.0003212109,0.6240485,0.2789155,0.0008053551,0.01668787,0.0001126332],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.551223,0.0006031683,0.4361577,0.0005238195,0.0002231212,0.00198127,0.0002973315,0.004235013,0.004755602],"genre_scores_gemma":[0.8790666,0.000183824,0.1161407,0.000245316,0.00003165264,0.0006153572,0.0002532391,0.00004892863,0.003414344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00230758,"threshold_uncertainty_score":0.007719696,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4379528766","doi":"10.1109/jbhi.2023.3282955","title":"A Scalable Federated Learning Approach for Collaborative Smart Healthcare Systems With Intermittent Clients Using Medical Imaging","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Scalability; Health care; Confidentiality; Big data; Machine learning; Deep learning; Process (computing); Information privacy; Artificial intelligence; Server; Computer security; Data science; Data mining; World Wide Web; Database","authors":[{"name":"Farhan Ullah","is_ca":false},{"name":"Gautam Srivastava","is_ca":true},{"name":"Heng Xiao","is_ca":false},{"name":"Shamsher Ullah","is_ca":false},{"name":"Jerry Chun‐Wei Lin","is_ca":false},{"name":"Yue Zhao","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06659481554619057,"gpt":0.3414329719560927,"spread":0.2748381564099021,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002793633,0.0005674146,0.001058521,0.0005026025,0.0009977235,0.001261784,0.002447664,0.001282467,0.001978745],"category_scores_gemma":[0.003386166,0.0003221056,0.0006646633,0.0006143803,0.0007400457,0.002252998,0.002809468,0.001185125,0.0004581646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001165866,"about_ca_system_score_gemma":0.001913573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004321529,"about_ca_topic_score_gemma":0.004128506,"domain_scores_codex":[0.9985966,0.0003753416,0.00008531809,0.0004268924,0.0002876411,0.000228249],"domain_scores_gemma":[0.9984316,0.0004782221,0.0001295177,0.0004620334,0.0003088915,0.0001897257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006971481,0.0007091413,0.005908413,0.00009367926,0.0001195998,0.0005254026,0.000462637,0.6466993,0.00799717,0.01128848,0.005731096,0.319768],"study_design_scores_gemma":[0.00001441247,0.00003699341,0.0001867758,0.000002569755,0.00000690254,0.00003252921,0.00003766358,0.994342,0.0009686192,0.00386622,0.0005002035,0.00000512605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05166823,0.0001592778,0.9433779,0.000502312,0.00003947433,0.0001153613,0.00006536992,0.002657939,0.001414159],"genre_scores_gemma":[0.8248932,0.00006880352,0.1721128,0.000261178,0.00003271949,0.000134581,0.0001912476,0.00006575015,0.00223968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004321529,"threshold_uncertainty_score":0.01477438,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4281784038","doi":"10.1109/jbhi.2022.3178629","title":"Securing Multimedia Using a Deep Learning Based Chaotic Logistic Map","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Encryption; Logistic map; Multimedia; Secure transmission; Cryptography; Chaotic; Artificial intelligence; Data mining; Computer security","authors":[{"name":"Ch. Rupa","is_ca":false},{"name":"M Harshitha","is_ca":false},{"name":"Gautam Srivastava","is_ca":true},{"name":"Thippa Reddy Gadekallu","is_ca":false},{"name":"Praveen Kumar Reddy Maddikunta","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04845675744000295,"gpt":0.3179304757346369,"spread":0.269473718294634,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023446,0.0003204,0.0002704464,0.0002454205,0.0001842427,0.0003763387,0.0003715182,0.0004178502,0.0008286064],"category_scores_gemma":[0.0006232561,0.00015816,0.0003857482,0.0001946141,0.0003136948,0.0005311141,0.0004804646,0.0004645769,0.000215063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000350723,"about_ca_system_score_gemma":0.00032073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001458069,"about_ca_topic_score_gemma":0.0009735962,"domain_scores_codex":[0.9998709,0.00002725604,0.000008897424,0.00002791025,0.00004351551,0.00002146836],"domain_scores_gemma":[0.999844,0.00006340253,0.00002497257,0.00001796324,0.00004133369,0.000008307019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003031857,0.0001226652,0.002599196,0.00015741,0.00009711694,0.0004826341,0.0001280134,0.7164248,0.08391453,0.009033521,0.001062762,0.1856741],"study_design_scores_gemma":[0.000003256074,0.00004992341,0.0002350067,0.000004132685,0.000006833552,0.00004804555,0.000005442023,0.9923403,0.006158014,0.0008131114,0.0003303891,0.000005463209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1726162,0.0005490196,0.8211009,0.0004005079,0.00007103114,0.00005739597,0.00008159028,0.0007217649,0.004401577],"genre_scores_gemma":[0.9540931,0.0003130723,0.04226725,0.00004475873,0.00001695514,0.00003735006,0.00005564792,0.00001498684,0.003156919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001458069,"threshold_uncertainty_score":0.00289911,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2599956891","doi":"10.1109/jbhi.2018.2831680","title":"Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":57,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Jaccard index; Convolutional neural network; Pattern recognition (psychology); Segmentation; Feature (linguistics); Feature extraction; Receiver operating characteristic; Test set; Ranking (information retrieval)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.04307519587842562,"gpt":0.3704896838341264,"spread":0.3274144879557008,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008589829,0.001379192,0.0005555292,0.0008963302,0.000272685,0.0007742001,0.001194619,0.001225203,0.001858247],"category_scores_gemma":[0.002596613,0.0004256898,0.0006782836,0.0006197926,0.0003343312,0.001049307,0.0006992549,0.001123249,0.001149327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001267291,"about_ca_system_score_gemma":0.0009818111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01479241,"about_ca_topic_score_gemma":0.01996335,"domain_scores_codex":[0.9994711,0.00008734367,0.00003127069,0.0001509917,0.0001529528,0.0001063882],"domain_scores_gemma":[0.999283,0.0002431614,0.00009270102,0.0001085793,0.0002333417,0.00003922244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007014705,0.0003805567,0.007462549,0.0002158359,0.0002560402,0.0002607965,0.0001061829,0.4075235,0.03035667,0.003907789,0.01966517,0.5291634],"study_design_scores_gemma":[0.000009553037,0.0000441725,0.001541955,0.00001613318,0.00002519825,0.00005074646,0.000009310415,0.9904647,0.004912865,0.001837748,0.00107677,0.00001093188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3672206,0.00487103,0.6001409,0.001665629,0.0004662755,0.0002795641,0.00340608,0.008691205,0.01325872],"genre_scores_gemma":[0.8676835,0.0009453191,0.1136907,0.0005355184,0.0001417243,0.0001332049,0.006507449,0.0001760382,0.01018654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01479241,"threshold_uncertainty_score":0.02941263,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3021508389","doi":"10.1109/jbhi.2020.2992878","title":"Multi-Task Pre-Training of Deep Neural Networks for Digital Pathology","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"AI in cancer detection","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Innovation for Defence Excellence and Security","keywords":"Feature extraction; Digital pathology; Feature (linguistics); Pattern recognition (psychology); Deep learning; Artificial neural network; Feature selection; Scheme (mathematics)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.05753932395557065,"gpt":0.3197855143351173,"spread":0.2622461903795467,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001970576,0.001796042,0.0008925782,0.0007802295,0.0005211736,0.0009679612,0.002175842,0.001698056,0.005169452],"category_scores_gemma":[0.005275275,0.0007287574,0.001031129,0.0007900414,0.0006330675,0.002355359,0.002030326,0.003539184,0.002565313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001013093,"about_ca_system_score_gemma":0.001481418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004456706,"about_ca_topic_score_gemma":0.009046112,"domain_scores_codex":[0.9992969,0.0001426637,0.00003580351,0.0002117532,0.0001521167,0.0001606945],"domain_scores_gemma":[0.998163,0.0007771977,0.0001381344,0.0003978132,0.0004174943,0.0001063317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008700844,0.001245903,0.005494474,0.0003837397,0.0002137262,0.0003718222,0.0002088394,0.3648579,0.03863359,0.003413002,0.01533459,0.5689724],"study_design_scores_gemma":[0.00003038434,0.0002742303,0.001545868,0.00003367923,0.00003506647,0.00009787712,0.00005804023,0.9665808,0.0241823,0.003779725,0.003358752,0.00002326427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1623652,0.001339139,0.8178247,0.000794184,0.000349842,0.0003979551,0.000845615,0.01018945,0.005893933],"genre_scores_gemma":[0.7265272,0.0003735893,0.2571728,0.0009695956,0.0001342416,0.0005801825,0.003438013,0.0005108979,0.01029357],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005169452,"threshold_uncertainty_score":0.01729351,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4376121360","doi":"10.1109/jbhi.2023.3274531","title":"Graph Self-Supervised Learning With Application to Brain Networks Analysis","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":52,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Autoencoder; Supervised learning; Machine learning; Graph; Autism spectrum disorder; Deep learning; Semi-supervised learning; Classifier (UML); Feature learning; Pattern recognition (psychology); Artificial neural network; Autism; Theoretical computer science; Medicine","authors":[{"name":"Guangqi Wen","is_ca":false},{"name":"Peng Cao","is_ca":false},{"name":"Lingwen Liu","is_ca":false},{"name":"Jinzhu Yang","is_ca":false},{"name":"Xizhe Zhang","is_ca":false},{"name":"Fei Wang","is_ca":false},{"name":"Osmar R. Zai͏̈ane","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02963022230877861,"gpt":0.3034260781502217,"spread":0.2737958558414431,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001261495,0.0006166429,0.0005427594,0.000680362,0.0002662775,0.0004367208,0.000817364,0.0008531522,0.0007870178],"category_scores_gemma":[0.003579466,0.0003403727,0.000600575,0.0005010169,0.0007466304,0.0007931946,0.0008813856,0.0009623849,0.0002079418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005781834,"about_ca_system_score_gemma":0.0008261383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003549622,"about_ca_topic_score_gemma":0.004270124,"domain_scores_codex":[0.9995452,0.0001845225,0.00002068923,0.0001169382,0.00009668234,0.00003594983],"domain_scores_gemma":[0.998162,0.0009584176,0.0001747358,0.0002586984,0.0003834075,0.00006288727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008709022,0.0001050524,0.001408254,0.00007604349,0.00009411401,0.0000982004,0.00009961774,0.8321598,0.005719862,0.006367821,0.0015972,0.1521869],"study_design_scores_gemma":[0.000001467519,0.000007608277,0.00007300822,0.000001114846,0.00000157424,0.000005310587,0.000001821921,0.9976284,0.0004753448,0.001709889,0.00009312282,0.000001398435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02758281,0.0002417938,0.9703588,0.0001981639,0.00002951895,0.00003602864,0.00005725329,0.000965907,0.0005297682],"genre_scores_gemma":[0.6446643,0.0003093156,0.3519727,0.0001774592,0.00007716171,0.0001354642,0.0003268344,0.0001799552,0.002156846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003549622,"threshold_uncertainty_score":0.007057965,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4321608070","doi":"10.1109/jbhi.2023.3248489","title":"Computerized Diagnosis of Liver Tumors From CT Scans Using a Deep Neural Network Approach","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Hepatocellular Carcinoma Treatment and Prognosis","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"National Cancer Institute; National Institutes of Health; Memorial Sloan-Kettering Cancer Center","keywords":"Intrahepatic Cholangiocarcinoma; Medicine; Hepatocellular carcinoma; Radiology; Computed tomography; Liver tumor; Metastasis; Cancer; Pathology; Internal medicine","authors":[{"name":"Abhishek Midya","is_ca":false},{"name":"Jayasree Chakraborty","is_ca":false},{"name":"Rami Srouji","is_ca":false},{"name":"Raja R. Narayan","is_ca":false},{"name":"Thomas Boerner","is_ca":false},{"name":"Jian Zheng","is_ca":false},{"name":"Linda M. Pak","is_ca":false},{"name":"John M. Creasy","is_ca":false},{"name":"Luz Adriana Escobar","is_ca":false},{"name":"Kate A. Harrington","is_ca":false},{"name":"Mithat Gönen","is_ca":false},{"name":"Michael I. D’Angelica","is_ca":false},{"name":"T. Peter Kingham","is_ca":false},{"name":"Richard Kinh Gian","is_ca":false},{"name":"William R. Jarnagin","is_ca":false},{"name":"Amber L. Simpson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1390869509872373,"gpt":0.310248120799083,"spread":0.1711611698118457,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003982061,0.0007070111,0.0004380447,0.001400074,0.0001990933,0.0005378346,0.0005980073,0.0006642843,0.0008313475],"category_scores_gemma":[0.001282557,0.0002239649,0.0004622375,0.0006749331,0.0001530508,0.0003661463,0.0005391532,0.0005479045,0.0003306655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005544614,"about_ca_system_score_gemma":0.0006338324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007171169,"about_ca_topic_score_gemma":0.01329745,"domain_scores_codex":[0.9997548,0.00005275184,0.00003014608,0.0000749253,0.00005359994,0.00003367949],"domain_scores_gemma":[0.9996412,0.0001269218,0.00006011678,0.00003749626,0.000110437,0.00002392316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006373867,0.0005906278,0.04911304,0.0001673668,0.0002301737,0.0006997153,0.0001053825,0.2185061,0.02632012,0.0009181139,0.004456665,0.6982552],"study_design_scores_gemma":[0.00001145844,0.00005921771,0.005126059,0.00001171332,0.00002884142,0.0001471532,0.0000241967,0.9895392,0.003881582,0.0007444932,0.0004149552,0.0000110885],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4863572,0.001219812,0.5038578,0.0006590959,0.00009857446,0.0003044234,0.002094021,0.002502745,0.002906379],"genre_scores_gemma":[0.8912717,0.0003466979,0.1038907,0.0001657857,0.00005290362,0.0001601923,0.00252971,0.00003127941,0.00155105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007171169,"threshold_uncertainty_score":0.0142588,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2394050630","doi":"10.1109/jbhi.2017.2657458","title":"SecureMed: Secure Medical Computation Using GPU-Accelerated Homomorphic Encryption Scheme","year":2017,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Homomorphic encryption; Computer science; Encryption; Speedup; NTRU; Cloud computing; Scheme (mathematics); Computer security; Parallel computing; Public-key cryptography; Operating system","authors":[{"name":"Alhassan Khedr","is_ca":true},{"name":"Glenn Gulak","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0984655627217598,"gpt":0.3725424013267138,"spread":0.274076838604954,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003665422,0.0003120447,0.0003951043,0.0003527146,0.0003516999,0.0007273947,0.000681872,0.0005388583,0.005461758],"category_scores_gemma":[0.0008302242,0.0001353991,0.0003481358,0.0003458091,0.0004324826,0.001113347,0.001123401,0.0006230248,0.00138551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005103289,"about_ca_system_score_gemma":0.0008385258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007871813,"about_ca_topic_score_gemma":0.001097396,"domain_scores_codex":[0.9996227,0.00008393408,0.0000255448,0.00003460538,0.0001658567,0.00006730145],"domain_scores_gemma":[0.9997237,0.00004464036,0.00002347926,0.0001363908,0.00004757301,0.00002421626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003656511,0.0005646489,0.004817426,0.0005653071,0.0002256162,0.001692884,0.0005687165,0.1181646,0.1800759,0.163072,0.05828188,0.4683146],"study_design_scores_gemma":[0.000376984,0.0004331165,0.00140032,0.00004670606,0.00003274438,0.001210048,0.00006796612,0.818141,0.1052587,0.02670822,0.04626363,0.00006057728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1450896,0.001340121,0.8120012,0.00123606,0.0003442578,0.0003716646,0.0007490837,0.01178511,0.02708283],"genre_scores_gemma":[0.7692168,0.000351285,0.2169648,0.0003334182,0.00005032455,0.0001528154,0.0007520592,0.0001972557,0.01198123],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005461758,"threshold_uncertainty_score":0.01827139,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2040674050","doi":"10.1109/jbhi.2013.2267494","title":"Smith Predictor-Based Robot Control for Ultrasound-Guided Teleoperated Beating-Heart Surgery","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Cardiac and Coronary Surgery Techniques","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Teleoperation; Robot; Computer vision; Computer science; Robot end effector; Artificial intelligence; Surgical robot; Robotic surgery; Telerobotics; Simulation; Surgery; Medicine; Mobile robot","authors":[{"name":"Meaghan Bowthorpe","is_ca":true},{"name":"Mahdi Tavakoli","is_ca":true},{"name":"Harald Becher","is_ca":true},{"name":"Robert D. Howe","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03392956276269996,"gpt":0.3095368867559089,"spread":0.2756073239932089,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004923205,0.0003421641,0.000414782,0.0002192444,0.0003074366,0.0005740725,0.000490261,0.0004253896,0.002113647],"category_scores_gemma":[0.001231324,0.000239051,0.0001739288,0.0002166023,0.000323083,0.0004033906,0.0002939042,0.0005956659,0.0003993979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004008227,"about_ca_system_score_gemma":0.0008768314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004305324,"about_ca_topic_score_gemma":0.005159762,"domain_scores_codex":[0.9997254,0.0000667852,0.00001637844,0.00005168809,0.0001144469,0.00002538342],"domain_scores_gemma":[0.9994279,0.0002376722,0.0001075252,0.0000352854,0.0001645111,0.00002705374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009577522,0.00023464,0.00322329,0.0002381743,0.000101522,0.0001978722,0.0002981639,0.5206338,0.07010092,0.004256894,0.001783574,0.3979734],"study_design_scores_gemma":[0.00002717733,0.0003871685,0.0008709652,0.00001339748,0.00001965402,0.00005097845,0.00001491967,0.9856144,0.01139804,0.0003567375,0.001232744,0.00001386239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05190802,0.0003388064,0.9435775,0.0001121638,0.000105206,0.00005092762,0.00001676662,0.001392203,0.002498368],"genre_scores_gemma":[0.925832,0.0002372922,0.06936473,0.00005455556,0.00003544427,0.0000585876,0.00003051474,0.00003421565,0.004352715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004305324,"threshold_uncertainty_score":0.008560538,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}