{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":27,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":27,"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":"4a64dadeb78e","filters":{"venue":"Journal of Healthcare Informatics Research"}},"results":[{"id":"W2805748155","doi":"10.1007/s41666-018-0026-9","title":"Developing Culturally Relevant Design Guidelines for Encouraging Physical Activity: a Social Cognitive Theory Perspective","year":2018,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Obesity, Physical Activity, Diet","field":"Medicine","cited_by":47,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Perspective (graphical); Social cognitive theory; Cognition; Psychology; Sociology; Social psychology; Computer science","authors":[{"name":"Kiemute Oyibo","is_ca":true},{"name":"Rita Orji","is_ca":true},{"name":"Julita Vassileva","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3292901995298164,"gpt":0.5390349239515217,"spread":0.2097447244217053,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04135844,0.001608533,0.001064465,0.003049016,0.002911099,0.008319621,0.004449038,0.004258526,0.002560246],"category_scores_gemma":[0.07843056,0.001176242,0.001029204,0.001472422,0.00771251,0.004069333,0.004003074,0.00603299,0.0006402346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00500877,"about_ca_system_score_gemma":0.01448808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01061849,"about_ca_topic_score_gemma":0.01854698,"domain_scores_codex":[0.9661882,0.02449312,0.002145419,0.001083873,0.005002517,0.001086828],"domain_scores_gemma":[0.9242862,0.05663992,0.003505465,0.00303083,0.01044645,0.002091117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003419559,0.006251951,0.03482907,0.008923875,0.0005558432,0.001487477,0.235931,0.02838885,0.01615915,0.317153,0.02058375,0.3293943],"study_design_scores_gemma":[0.0007504921,0.001822744,0.01938855,0.0126413,0.0009847762,0.001015327,0.2012377,0.03948528,0.02360523,0.5113895,0.1870138,0.0006653699],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1262429,0.002825856,0.728337,0.06422036,0.0006974166,0.003760378,0.0002965116,0.001136448,0.07248314],"genre_scores_gemma":[0.3983098,0.001499512,0.5900148,0.004855318,0.00006834676,0.003115707,0.0001404689,0.0001700788,0.001825876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04135844,"threshold_uncertainty_score":0.2187269,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3159236487","doi":"10.1007/s41666-021-00095-7","title":"A Pilot Study to Detect Agitation in People Living with Dementia Using Multi-Modal Sensors","year":2021,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"ECW Press (Canada); Toronto Rehabilitation Institute; University of Toronto; Toronto Metropolitan University; University Health Network","funders":"","keywords":"Dementia; Modal; Wearable computer; Distress; Psychology; Aggression; Computer science; Medicine; Psychiatry; Clinical psychology; Embedded system; Internal medicine; Disease","authors":[{"name":"Sofija Spasojević","is_ca":true},{"name":"Jacob Nogas","is_ca":true},{"name":"Andrea Iaboni","is_ca":true},{"name":"B. Ye","is_ca":true},{"name":"Alex Mihailidis","is_ca":true},{"name":"A. Wang","is_ca":true},{"name":"Sisheng Li","is_ca":true},{"name":"L.S. Martin Martin","is_ca":true},{"name":"Kristine Newman","is_ca":true},{"name":"S. S. Khan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1328278398924288,"gpt":0.4622607448830768,"spread":0.329432904990648,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00185647,0.0007049426,0.0005668665,0.0004199088,0.0008404332,0.0004466158,0.0004877502,0.0007875917,0.002551373],"category_scores_gemma":[0.003537271,0.0003039841,0.0008307612,0.0001970339,0.0005320979,0.000723858,0.0005515153,0.0007621846,0.0006704709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003163594,"about_ca_system_score_gemma":0.001056319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003691371,"about_ca_topic_score_gemma":0.004310126,"domain_scores_codex":[0.999414,0.0002582065,0.00006300231,0.00008882394,0.00007260475,0.0001034848],"domain_scores_gemma":[0.9974768,0.0009023364,0.0001064874,0.0002356916,0.0007689062,0.0005097691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.04739523,0.2704081,0.4310362,0.00220045,0.001162612,0.005930471,0.01529245,0.001808171,0.1049896,0.0005185777,0.00299924,0.116259],"study_design_scores_gemma":[0.009395869,0.5860167,0.3668682,0.0001334469,0.0008728587,0.001906308,0.01236625,0.004967163,0.01352337,0.000355767,0.003469596,0.0001243906],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966866,0.00002666232,0.0009674031,0.00004030789,0.00003787984,0.00165921,0.0001840523,0.00002179025,0.0003760358],"genre_scores_gemma":[0.9898474,0.00009870399,0.004386271,0.0001837321,0.00005276276,0.003945489,0.0004671453,0.000007186859,0.001011338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003691371,"threshold_uncertainty_score":0.009818077,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3127200664","doi":"10.1007/s41666-020-00087-z","title":"Scoping Review of Healthcare Literature on Mobile, Wearable, and Textile Sensing Technology for Continuous Monitoring","year":2021,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":38,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"International Collaboration On Repair Discoveries; University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Wearable computer; Health care; Continuous monitoring; Psychological intervention; Wearable technology; Health technology; Computer science; Medical diagnosis; Medicine; Engineering; Operations management; Nursing; Embedded system","authors":[{"name":"Netzahualcoyotl Hernandez-Cruz","is_ca":false},{"name":"Luís A. Castro","is_ca":false},{"name":"Javier Medina-Quero","is_ca":false},{"name":"Jesús Favela","is_ca":false},{"name":"Layla Michán","is_ca":false},{"name":"W. Ben Mortenson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1184206257967176,"gpt":0.5484974127628792,"spread":0.4300767869661616,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01115159,0.001412786,0.003395838,0.01574575,0.001187503,0.005196372,0.002166318,0.003650479,0.005612165],"category_scores_gemma":[0.04595884,0.0008826779,0.004111567,0.01388372,0.001923175,0.003423371,0.002267526,0.002043953,0.001033409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002562417,"about_ca_system_score_gemma":0.01308709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007858268,"about_ca_topic_score_gemma":0.01423928,"domain_scores_codex":[0.9922161,0.001871975,0.003140165,0.0008015818,0.001693723,0.0002765687],"domain_scores_gemma":[0.935158,0.05049776,0.006036002,0.0008327097,0.007038802,0.0004368526],"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":[0.0002943689,0.0001167254,0.00182659,0.5891469,0.001914068,0.0002857184,0.0009930258,0.0002355598,0.001025102,0.002069419,0.01026991,0.3918227],"study_design_scores_gemma":[0.00004576478,0.0002039323,0.003876497,0.8714349,0.007082661,0.0006799115,0.001199329,0.0001490014,0.0005827346,0.001498331,0.113207,0.00003991972],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005191119,0.9973648,0.0002373434,0.0007466298,0.0001873838,0.00006231618,0.0001109365,0.000005711562,0.0007657919],"genre_scores_gemma":[0.004512327,0.9932489,0.0006125896,0.0009057235,0.000240004,0.0001297799,0.0001505338,0.00000612438,0.0001939686],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01574575,"threshold_uncertainty_score":0.05897599,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2995241421","doi":"10.1007/s41666-019-00063-2","title":"Predicting Glycaemia in Type 1 Diabetes Patients: Experiments in Feature Engineering and Data Imputation","year":2019,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Diabetes Management and Research","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto East General Hospital","funders":"","keywords":"Imputation (statistics); Missing data; Computer science; Predictive modelling; Type 2 diabetes; Diabetes mellitus; Statistics; Medicine; Machine learning; Mathematics; Endocrinology","authors":[{"name":"Jouhyun Jeon","is_ca":true},{"name":"Peter Leimbigler","is_ca":true},{"name":"Gaurav Baruah","is_ca":true},{"name":"Michael H. Li","is_ca":true},{"name":"Yan Fossat","is_ca":true},{"name":"Alfred J. Whitehead","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06161369520299682,"gpt":0.4166492730689366,"spread":0.3550355778659398,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007303466,0.001186324,0.001630398,0.0006695435,0.0005257094,0.0009772577,0.001280663,0.001901227,0.0009862079],"category_scores_gemma":[0.018019,0.0004024743,0.001738594,0.001048887,0.0005344073,0.001197292,0.0006971031,0.002121306,0.0003659853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005670116,"about_ca_system_score_gemma":0.001298936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01182566,"about_ca_topic_score_gemma":0.005745548,"domain_scores_codex":[0.997111,0.00160846,0.0002352641,0.0005184492,0.0002733091,0.0002535451],"domain_scores_gemma":[0.9770126,0.01916688,0.0006194549,0.001901225,0.000933549,0.000366245],"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":[0.01683599,0.012406,0.1636525,0.0005514993,0.001975916,0.0006753341,0.0005465003,0.4768487,0.006673056,0.00142663,0.01044387,0.307964],"study_design_scores_gemma":[0.0007841666,0.002742677,0.02493724,0.00003076944,0.0002901327,0.0001886425,0.0001907646,0.9638005,0.004269769,0.001886737,0.0008148698,0.00006373977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779066,0.0005627774,0.01731548,0.0007887715,0.0001781257,0.000118063,0.001469148,0.0007936588,0.0008673588],"genre_scores_gemma":[0.971405,0.0001656281,0.02461205,0.0001608102,0.00005075689,0.00007083909,0.002823121,0.00002762772,0.000684126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01182566,"threshold_uncertainty_score":0.03862488,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3179877945","doi":"10.1007/s41666-021-00102-x","title":"Investigating Public Discourses Around Gender and COVID-19: a Social Media Analysis of Twitter Data","year":2021,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":35,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research","keywords":"Influencer marketing; Sentiment analysis; Social media; Pandemic; Content analysis; Coronavirus disease 2019 (COVID-19); Critical discourse analysis; Tracking (education); Psychology; Political science; Gender studies; Sociology; Medicine; Politics; Social science; Computer science; Business","authors":[{"name":"Ahmed Al‐Rawi","is_ca":true},{"name":"Karen A. Grépin","is_ca":false},{"name":"Xiaosu Li","is_ca":true},{"name":"Rosemary Morgan","is_ca":false},{"name":"Clare Wenham","is_ca":false},{"name":"Julia Smith","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.6373609750149246,"gpt":0.5774138998398567,"spread":0.05994707517506792,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005241626,0.0003051522,0.0002863371,0.003408631,0.002824306,0.005479376,0.0005423279,0.001393734,0.003519603],"category_scores_gemma":[0.02941447,0.0002307466,0.0002547447,0.00496036,0.00196334,0.006965045,0.003775155,0.001543474,0.001037806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001646508,"about_ca_system_score_gemma":0.00180296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007695297,"about_ca_topic_score_gemma":0.0112486,"domain_scores_codex":[0.9955372,0.002554414,0.0002914722,0.0003401716,0.0008335196,0.000443365],"domain_scores_gemma":[0.9544973,0.03535276,0.004621157,0.001486894,0.003046988,0.0009949745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0006966662,0.0002846328,0.2285322,0.0006646101,0.00009229411,0.001098211,0.6682466,0.0003312721,0.008262428,0.01689748,0.0159472,0.05894645],"study_design_scores_gemma":[0.00002017146,0.000139666,0.1696908,0.0005489844,0.00007891308,0.0003829383,0.7284278,0.002579152,0.003508354,0.003875439,0.09064308,0.0001047691],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805705,0.0002252744,0.001068565,0.004161734,0.0001141066,0.00006280307,0.00198833,0.00003236484,0.01177635],"genre_scores_gemma":[0.9932626,0.0002792682,0.0009588468,0.0006375585,0.0001627952,0.0001339232,0.001536871,0.00006655564,0.002961534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007695297,"threshold_uncertainty_score":0.02772075,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3080274575","doi":"10.1007/s41666-021-00111-w","title":"COVID-19 Pandemic: Identifying Key Issues Using Social Media and Natural Language Processing","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan; Dalhousie University","funders":"Dalhousie University; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Compute Canada","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Sentiment analysis; Social media; Thematic analysis; Politics; Perception; Political science; Public relations; Sociology; Psychology; Social science; Qualitative research; Computer science; Medicine; Disease; Natural language processing","authors":[{"name":"Oladapo Oyebode","is_ca":true},{"name":"Chinenye Ndulue","is_ca":true},{"name":"Dinesh Mulchandani","is_ca":true},{"name":"Banuchitra Suruliraj","is_ca":true},{"name":"Ashfaq Adib","is_ca":true},{"name":"Fidelia A. Orji","is_ca":true},{"name":"Evangelos Milios","is_ca":true},{"name":"Stan Matwin","is_ca":true},{"name":"Rita Orji","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3688109419020878,"gpt":0.5754048449226615,"spread":0.2065939030205737,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003994112,0.0006341366,0.000401174,0.006507271,0.001453333,0.00301927,0.001028137,0.002064061,0.003255605],"category_scores_gemma":[0.01813712,0.0002421622,0.0004909517,0.003117486,0.0009870803,0.004721321,0.002339822,0.00193871,0.001137317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001437942,"about_ca_system_score_gemma":0.003679313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0209088,"about_ca_topic_score_gemma":0.02160784,"domain_scores_codex":[0.9968906,0.001038218,0.0003623763,0.0003289078,0.0009832924,0.0003966292],"domain_scores_gemma":[0.9819302,0.01185569,0.002002506,0.0008826871,0.002442917,0.000885975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001112306,0.001507294,0.4040511,0.002692739,0.0003851996,0.003078674,0.006349597,0.005724799,0.01306953,0.01632842,0.2445064,0.3011939],"study_design_scores_gemma":[0.0002268064,0.001212264,0.4227939,0.00254793,0.000655086,0.003437524,0.08144646,0.1386545,0.02446513,0.03621005,0.2878485,0.0005019405],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7304402,0.004973721,0.01977564,0.07297769,0.002792862,0.003008767,0.1190818,0.002295934,0.04465333],"genre_scores_gemma":[0.8809621,0.002428719,0.03255058,0.006030606,0.001341456,0.0008971563,0.07074086,0.0002040131,0.004844487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0209088,"threshold_uncertainty_score":0.04157418,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3137246643","doi":"10.1007/s41666-021-00097-5","title":"Analyzing Patient Stories on Social Media Using Text Analytics","year":2021,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Patient Satisfaction in Healthcare","field":"Health Professions","cited_by":31,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University; Regional Municipality of Waterloo; University of Waterloo","funders":"","keywords":"Social media; Health care; Latent Dirichlet allocation; Quality (philosophy); Sentiment analysis; Topic model; Analytics; Workload; Psychology; Computer science; Data science; World Wide Web; Artificial intelligence; Political science","authors":[{"name":"Moutasem Zakkar","is_ca":true},{"name":"Daniel J. Lizotte","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4561559312256841,"gpt":0.5765540087634238,"spread":0.1203980775377397,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001187353,0.0006164373,0.0002588101,0.005852077,0.0006257685,0.002374391,0.0004065322,0.0006493828,0.002992011],"category_scores_gemma":[0.01269701,0.0001467139,0.000360374,0.003416889,0.0003170962,0.002728899,0.001106429,0.0006522413,0.001311144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003737573,"about_ca_system_score_gemma":0.0004470345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001232879,"about_ca_topic_score_gemma":0.002518161,"domain_scores_codex":[0.9983191,0.0007325814,0.0001849917,0.0001658984,0.0004851575,0.0001122558],"domain_scores_gemma":[0.9733772,0.02211539,0.001842873,0.0005660639,0.001656382,0.0004419728],"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":[0.001508025,0.001340219,0.2853586,0.002651554,0.0004894397,0.005554555,0.05322241,0.004749315,0.03433818,0.003845159,0.04674959,0.5601929],"study_design_scores_gemma":[0.000112803,0.001294879,0.4018244,0.001781747,0.0008436705,0.007072874,0.1423475,0.1735932,0.04565739,0.01710603,0.2079915,0.0003740087],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9436519,0.001343876,0.0163742,0.002613069,0.0003953391,0.0003663074,0.02141587,0.0009579391,0.01288153],"genre_scores_gemma":[0.9590021,0.0009645259,0.0199112,0.0003218674,0.0005376123,0.0002549196,0.01500568,0.0001651061,0.003837036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005852077,"threshold_uncertainty_score":0.01000923,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4317605960","doi":"10.1007/s41666-022-00123-0","title":"Electronic Health Records That Support Health Professional Reflective Practice: a Missed Opportunity in Digital Health","year":2022,"lang":"en","type":"editorial","venue":"Journal of Healthcare Informatics Research","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":28,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; University Health Network","funders":"Australian Government","keywords":"Underpinning; Health care; Health professionals; Digital health; Reflection (computer programming); Reflective practice; Clinical decision support system; Knowledge management; Data science; Psychology; Computer science; Engineering; Political science","authors":[{"name":"Anna Janssen","is_ca":false},{"name":"Judy Kay","is_ca":false},{"name":"Stella Talic","is_ca":false},{"name":"Martin Pusic","is_ca":false},{"name":"Robert Birnbaum","is_ca":false},{"name":"Rodrigo B. Cavalcanti","is_ca":true},{"name":"Dragan Gašević","is_ca":false},{"name":"Tim Shaw","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.211013019208863,"gpt":0.5861326619935591,"spread":0.375119642784696,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028029,0.002072372,0.003358455,0.004067367,0.006698938,0.01675859,0.005942686,0.03317416,0.007641132],"category_scores_gemma":[0.1143566,0.001413984,0.002931804,0.002487263,0.00889766,0.01189679,0.004562396,0.03401364,0.005903265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005202427,"about_ca_system_score_gemma":0.009232777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002607345,"about_ca_topic_score_gemma":0.005443309,"domain_scores_codex":[0.9716226,0.00884636,0.003559079,0.001918301,0.01302277,0.001030708],"domain_scores_gemma":[0.8201948,0.1240374,0.005142883,0.004423433,0.03729608,0.008905358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003344535,0.00001677784,0.00002649297,0.0007169008,0.00002096782,0.0001389409,0.0002368162,0.00003288364,0.00006075004,0.001580846,0.9825712,0.01456405],"study_design_scores_gemma":[0.00004724348,0.00003016975,0.0001005683,0.002017816,0.00003555574,0.0002868504,0.0002447651,0.0001340513,0.00007480641,0.002840667,0.9941613,0.00002610412],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00003555216,0.008202153,0.0002765007,0.087971,0.9026608,0.00002071853,0.00001499985,0.00004420923,0.0007739433],"genre_scores_gemma":[0.0004720901,0.007386908,0.0002752217,0.03993366,0.9492779,0.00003374125,0.00001336133,0.00004400252,0.002563082],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.03317416,"threshold_uncertainty_score":0.1482332,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4391878938","doi":"10.1007/s41666-024-00160-x","title":"Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction","year":2024,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":21,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electroencephalography; Epilepsy; Artificial intelligence; Deep learning; Computer science; Epileptic seizure; Machine learning; Supervised learning; Pattern recognition (psychology); Psychology; Neuroscience; Artificial neural network","authors":[{"name":"Zakary Georgis-Yap","is_ca":true},{"name":"Miloš R. Popović","is_ca":true},{"name":"Shehroz S. Khan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1902143672251172,"gpt":0.3984990901101877,"spread":0.2082847228850706,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001024109,0.0007685584,0.0006493816,0.0007794441,0.0003104024,0.0005971133,0.0009029408,0.0007847093,0.001073144],"category_scores_gemma":[0.0022709,0.0003093138,0.0007768784,0.000640353,0.0002564511,0.0008480286,0.0008113683,0.001247194,0.0003811489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000378035,"about_ca_system_score_gemma":0.0009751567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005049564,"about_ca_topic_score_gemma":0.00942392,"domain_scores_codex":[0.9996549,0.0001099969,0.0000324446,0.00007884274,0.0000611002,0.00006276259],"domain_scores_gemma":[0.9988055,0.0006601237,0.00008633205,0.0001032778,0.0003021198,0.00004273074],"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":[0.0003360841,0.0005224136,0.006183691,0.0001245416,0.000199931,0.0001068,0.00007889553,0.3266242,0.007278463,0.003635605,0.006046172,0.6488632],"study_design_scores_gemma":[0.000005411903,0.00002810154,0.0007114692,0.000005897136,0.00001189219,0.00001384876,0.000008413313,0.996425,0.0009343002,0.001642046,0.0002095812,0.000004122443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1112187,0.002049445,0.8817388,0.0005716642,0.0001486756,0.00008304714,0.0005751651,0.001600466,0.002014013],"genre_scores_gemma":[0.8685426,0.0006374501,0.1221743,0.0001764301,0.0001813758,0.0001103913,0.00130474,0.00009754907,0.006774997],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005049564,"threshold_uncertainty_score":0.01004034,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4317750115","doi":"10.1007/s41666-023-00125-6","title":"Can Patients with Dementia Be Identified in Primary Care Electronic Medical Records Using Natural Language Processing?","year":2023,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Women's College Hospital; University of Toronto; University Health Network; Health Sciences Centre; Toronto Rehabilitation Institute; Sunnybrook Health Science Centre; Public Health Ontario","funders":"Alzheimer Society Research Program; Alzheimer Society; Canadian Institutes of Health Research; Government of Ontario","keywords":"Dementia; Medical record; Cognition; Cohort; Medicine; Artificial intelligence; MEDLINE; Psychology; Natural language processing; Machine learning; Psychiatry; Computer science; Disease; Internal medicine","authors":[{"name":"Laura C. Maclagan","is_ca":false},{"name":"Mohamed Abdalla","is_ca":true},{"name":"Daniel A. Harris","is_ca":true},{"name":"Thérèse A. Stukel","is_ca":true},{"name":"Branson Chen","is_ca":false},{"name":"Elisa Candido","is_ca":false},{"name":"Richard H. Swartz","is_ca":true},{"name":"Andrea Iaboni","is_ca":true},{"name":"R. Liisa Jaakkimainen","is_ca":true},{"name":"Susan E. Bronskill","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03382600970431461,"gpt":0.4133786813594748,"spread":0.3795526716551602,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003059832,0.0002801084,0.0005974339,0.003379827,0.0006149735,0.002569909,0.0006513626,0.001373186,0.002100604],"category_scores_gemma":[0.04648427,0.0002335791,0.0007678106,0.002496261,0.0004487256,0.003547272,0.0008064574,0.0008289851,0.0006928777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007719834,"about_ca_system_score_gemma":0.00158947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009971036,"about_ca_topic_score_gemma":0.01480589,"domain_scores_codex":[0.9969868,0.001134934,0.0007324597,0.0004436919,0.0004707103,0.0002313372],"domain_scores_gemma":[0.9806417,0.01284688,0.003574609,0.0005457465,0.002059342,0.0003317135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005480181,0.0004496597,0.9046497,0.001021005,0.0002166238,0.001426463,0.002179985,0.0002753709,0.001158265,0.0009055969,0.009770411,0.07739887],"study_design_scores_gemma":[0.0001870676,0.0005039533,0.9396605,0.001481181,0.0007390712,0.005041381,0.01391196,0.009694783,0.001966683,0.008172098,0.01851175,0.0001295672],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9492617,0.005675139,0.004501722,0.02013205,0.0004751285,0.0002688295,0.008688065,0.0001644382,0.01083302],"genre_scores_gemma":[0.9840439,0.001749866,0.005622425,0.003563258,0.0002832304,0.00012308,0.004004134,0.00001142647,0.0005987737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009971036,"threshold_uncertainty_score":0.01982599,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3017035905","doi":"10.1007/s41666-020-00071-7","title":"Comparative Analysis of Gait Speed Estimation Using Wideband and Narrowband Radars, Thermal Camera, and Motion Tracking Suit Technologies","year":2020,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada); Institute for Work & Health; University of Waterloo; Research Institute for Aging; Institute of Health Services and Policy Research; Blackberry (Canada); University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Narrowband; Computer science; Radar; Preferred walking speed; Gait; Computer vision; Artificial intelligence; Match moving; Simulation; Telecommunications; Motion (physics); Physical medicine and rehabilitation; Medicine","authors":[{"name":"Plinio Pelegrini Morita","is_ca":true},{"name":"Adson Silva Rocha","is_ca":true},{"name":"George Shaker","is_ca":true},{"name":"D. Lee","is_ca":true},{"name":"Jing Wei","is_ca":true},{"name":"B. Fong","is_ca":true},{"name":"A Thatte","is_ca":true},{"name":"Anahita Karimi","is_ca":true},{"name":"Lin Xu","is_ca":true},{"name":"Aoxiang Ma","is_ca":true},{"name":"Alexander Wong","is_ca":true},{"name":"Jennifer Boger","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1273035509863732,"gpt":0.3859717666578343,"spread":0.2586682156714611,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001171293,0.0004079858,0.0004126327,0.001551181,0.0001101757,0.0004849894,0.000243421,0.0005123827,0.0005521328],"category_scores_gemma":[0.003711498,0.0001449703,0.000350339,0.0007108875,0.0001420014,0.0006197628,0.0003475485,0.0001328029,0.0002325238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001062197,"about_ca_system_score_gemma":0.00008538908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006477287,"about_ca_topic_score_gemma":0.000907378,"domain_scores_codex":[0.9991185,0.0002669825,0.00008605959,0.0001539416,0.0002980578,0.00007649672],"domain_scores_gemma":[0.9981769,0.0007231947,0.0002088464,0.0001196924,0.0007176034,0.00005371936],"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":[0.00665779,0.0004878491,0.3475513,0.001201771,0.0008557585,0.0007396959,0.001100565,0.01386777,0.2493903,0.000707024,0.001118777,0.3763213],"study_design_scores_gemma":[0.00005384561,0.004575679,0.8679256,0.00007638458,0.0006626355,0.001692373,0.0009375509,0.04746638,0.07429188,0.0002580961,0.001971633,0.00008791778],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846028,0.0004476132,0.01346389,0.00001943025,0.00001984606,0.00002801449,0.0002161628,0.00006929025,0.00113306],"genre_scores_gemma":[0.9933187,0.0002189303,0.00572593,0.00001448345,0.00001201365,0.00002031678,0.0002928996,0.00001229793,0.0003844492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001551181,"threshold_uncertainty_score":0.006194472,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4320913615","doi":"10.1007/s41666-023-00127-4","title":"Interpretable Skin Cancer Classification based on Incremental Domain Knowledge Learning","year":2023,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Hamilton Health Sciences; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Skin cancer; Artificial intelligence; Computer science; Machine learning; Workflow; Medicine; Cancer","authors":[{"name":"Eman Rezk","is_ca":true},{"name":"Mohamed Eltorki","is_ca":true},{"name":"Wael El‐Dakhakhni","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1166005407274459,"gpt":0.4574601236188124,"spread":0.3408595828913665,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007247138,0.0008115732,0.0009106193,0.001882637,0.0004110793,0.00130115,0.001515343,0.001030965,0.002182699],"category_scores_gemma":[0.003168496,0.0002682932,0.0009719071,0.0009048872,0.0002688089,0.001350296,0.001028758,0.001313515,0.000821966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005564478,"about_ca_system_score_gemma":0.00102788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005405844,"about_ca_topic_score_gemma":0.00758498,"domain_scores_codex":[0.999366,0.0001032803,0.00006535401,0.0002288258,0.000152804,0.0000836886],"domain_scores_gemma":[0.9981928,0.0009272118,0.00009987458,0.0002415927,0.000485741,0.00005278911],"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":[0.0004925077,0.0007681404,0.009471894,0.0002142949,0.0001863889,0.0005573217,0.0001481668,0.07062354,0.0122254,0.001595304,0.007215144,0.8965018],"study_design_scores_gemma":[0.00002484915,0.0001059373,0.002382872,0.00003839162,0.00009732678,0.0002200787,0.00007320363,0.9855589,0.005017929,0.005004152,0.001460257,0.00001614818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2376622,0.003104403,0.7430942,0.001162596,0.0003436484,0.0004033928,0.002546246,0.005471267,0.006211946],"genre_scores_gemma":[0.8390917,0.0005941555,0.1542256,0.0002716549,0.0001359004,0.0001396694,0.003508874,0.00006641977,0.001966023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005405844,"threshold_uncertainty_score":0.01074874,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3158606818","doi":"10.1007/s41666-022-00118-x","title":"Auto Response Generation in Online Medical Chat Services","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":16,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Mitacs","keywords":"Telehealth; Robustness (evolution); Computer science; Cluster analysis; Coronavirus disease 2019 (COVID-19); Machine learning; Artificial intelligence; Telemedicine; World Wide Web; Multimedia; Medicine; Health care","authors":[{"name":"Hadi Jahanshahi","is_ca":true},{"name":"Syed Jamil Hasan Kazmi","is_ca":true},{"name":"Mücahit Çevik","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1928242768846095,"gpt":0.551804997462196,"spread":0.3589807205775865,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003536582,0.0004635453,0.000363143,0.000784786,0.0006572502,0.001393066,0.0007750253,0.001234908,0.01343387],"category_scores_gemma":[0.02256572,0.0002485595,0.0002614851,0.0003322234,0.0003266225,0.001162968,0.001239649,0.0007020011,0.002228934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004229124,"about_ca_system_score_gemma":0.0006872729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001076568,"about_ca_topic_score_gemma":0.001036346,"domain_scores_codex":[0.9964573,0.00243703,0.0001175505,0.0002788704,0.0004659763,0.0002431713],"domain_scores_gemma":[0.9621851,0.03218719,0.001088214,0.001784967,0.001943148,0.0008113885],"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":[0.0172265,0.006464788,0.1506812,0.0008256018,0.0002165797,0.003350699,0.01195262,0.0230396,0.04954147,0.01203609,0.01504134,0.7096235],"study_design_scores_gemma":[0.0006517829,0.004944724,0.08385709,0.0003261208,0.0003335652,0.003209278,0.009507777,0.8064477,0.05787124,0.02221731,0.01042334,0.0002101582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9510501,0.00012327,0.03556455,0.0004057641,0.0001470811,0.0003752761,0.0002024585,0.002661807,0.009469813],"genre_scores_gemma":[0.9884452,0.00002440444,0.007818911,0.0001451185,0.00003180275,0.00009564562,0.000115746,0.0001047365,0.003218431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01343387,"threshold_uncertainty_score":0.04494077,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2803361099","doi":"10.1007/s41666-018-0021-1","title":"Machine Learning and Mobile Health Monitoring Platforms: A Case Study on Research and Implementation Challenges","year":2018,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Software deployment; Computer science; Server; Mobile device; Mobile computing; Machine learning; Artificial intelligence; Embedded system; Data science; Software engineering; World Wide Web; Operating system","authors":[{"name":"Omar Boursalie","is_ca":true},{"name":"Reza Samavi","is_ca":true},{"name":"Thomas E. Doyle","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3205157342527513,"gpt":0.5877039912834002,"spread":0.267188257030649,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02358343,0.0005228608,0.0004211042,0.001249405,0.002875847,0.00610992,0.002697936,0.004486668,0.003825988],"category_scores_gemma":[0.0497856,0.000435412,0.0005269827,0.001556033,0.002606249,0.006987058,0.004392825,0.003121164,0.001035369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002527227,"about_ca_system_score_gemma":0.00553935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003826599,"about_ca_topic_score_gemma":0.004996087,"domain_scores_codex":[0.9818864,0.01231018,0.0007256478,0.0009799396,0.002528161,0.001569737],"domain_scores_gemma":[0.942032,0.04265539,0.002663575,0.003778086,0.005225481,0.003645383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001435108,0.007382209,0.1620045,0.001763219,0.0001928162,0.02903101,0.1030769,0.01004248,0.01172635,0.09372,0.02218821,0.5574372],"study_design_scores_gemma":[0.0006788539,0.008785337,0.0763219,0.00435766,0.0004836672,0.02882506,0.3554103,0.09844774,0.02455987,0.07337181,0.3282379,0.0005198817],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8637322,0.001135826,0.06155474,0.03819224,0.0002570515,0.001355211,0.000322249,0.0003436718,0.03310682],"genre_scores_gemma":[0.9629734,0.0006978174,0.03060541,0.001581062,0.00007384653,0.0003677237,0.0001227091,0.00008430209,0.003493888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02358343,"threshold_uncertainty_score":0.1247226,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4386888280","doi":"10.1007/s41666-023-00138-1","title":"Clinical Feature Ranking Based on Ensemble Machine Learning Reveals Top Survival Factors for Glioblastoma Multiforme","year":2023,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Ministero dell'Università e della Ricerca; Università degli Studi di Milano-Bicocca; European Commission; Dipartimenti di Eccellenza","keywords":"Concordance; Random forest; Artificial intelligence; Machine learning; Context (archaeology); Computer science; Glioblastoma; Feature (linguistics); Decision tree; Medicine; Data mining; Internal medicine; Biology","authors":[{"name":"Gabriel Cerono","is_ca":false},{"name":"Ombretta Melaiu","is_ca":false},{"name":"Davide Chicco","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1123547609147775,"gpt":0.4821533126688797,"spread":0.3697985517541021,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001736685,0.0006149538,0.001187196,0.002446844,0.0003092215,0.0006799598,0.0003785711,0.0004007602,0.0006769123],"category_scores_gemma":[0.006354616,0.0001086635,0.001108294,0.00136955,0.0001566378,0.000484236,0.0003578303,0.0005658506,0.0003145249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003606683,"about_ca_system_score_gemma":0.0006995628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005923579,"about_ca_topic_score_gemma":0.007060238,"domain_scores_codex":[0.9992293,0.0002550665,0.00007027948,0.000170074,0.0001443483,0.0001308978],"domain_scores_gemma":[0.9977599,0.001246486,0.0002226911,0.0002101131,0.0004393146,0.0001214169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001189423,0.0004736382,0.5237858,0.0001974493,0.0008839807,0.0005580364,0.0001343409,0.1009045,0.00537884,0.0006539617,0.01368019,0.3521599],"study_design_scores_gemma":[0.00005317463,0.0004427893,0.1251146,0.00004771041,0.0004753373,0.0003832585,0.000128377,0.8646338,0.003721981,0.00293462,0.002000258,0.00006411309],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9635189,0.002267921,0.02971409,0.000610209,0.0001330342,0.00004993828,0.002192724,0.0005800258,0.0009331937],"genre_scores_gemma":[0.9912885,0.0002208732,0.005413631,0.00005753454,0.0000559512,0.00001611193,0.002729957,0.00001799165,0.0001993266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005923579,"threshold_uncertainty_score":0.01177818,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2809296704","doi":"10.1007/s41666-018-0028-7","title":"A Data Mining Framework for Glaucoma Decision Support Based on Optic Nerve Image Analysis Using Machine Learning Methods","year":2018,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Glaucoma and retinal disorders","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"","keywords":"Glaucoma; Optic nerve; Artificial intelligence; Computer science; Optic disc; Optic cup (embryology); Support vector machine; Optic disk; Naive Bayes classifier; Machine learning; Pattern recognition (psychology); Medicine; Ophthalmology","authors":[{"name":"Syed Sibte Raza Abidi","is_ca":true},{"name":"Patrice Roy","is_ca":true},{"name":"Muhammad Shadiq Bin Md Shah","is_ca":true},{"name":"Jin Yu","is_ca":true},{"name":"Sanjun Yan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1942404525781003,"gpt":0.5456114011488725,"spread":0.3513709485707722,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001636521,0.0007705667,0.001250302,0.002356021,0.0007055317,0.0019446,0.001582438,0.0009995507,0.001566473],"category_scores_gemma":[0.002957144,0.000349202,0.001533127,0.001200465,0.0002754495,0.001207389,0.001162448,0.001067091,0.0006668338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000681348,"about_ca_system_score_gemma":0.001932332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008312846,"about_ca_topic_score_gemma":0.008805626,"domain_scores_codex":[0.9992873,0.0001176761,0.0001173399,0.00016418,0.0002540703,0.00005940013],"domain_scores_gemma":[0.9988537,0.0005135485,0.00009493127,0.00006304715,0.0004003851,0.00007435743],"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":[0.0005100017,0.001109409,0.01208517,0.0005160828,0.0004755933,0.00110136,0.0003364414,0.2278109,0.01346976,0.01804833,0.01052517,0.7140118],"study_design_scores_gemma":[0.00001714215,0.0000419223,0.0006356676,0.0000318361,0.00004983446,0.0001068332,0.00003790378,0.9889874,0.002495735,0.005600545,0.001980378,0.00001473667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009529755,0.0002624146,0.9869149,0.000325285,0.00003254254,0.0001718676,0.0004258987,0.00188351,0.0004537719],"genre_scores_gemma":[0.1512361,0.0002901137,0.8459095,0.0001528539,0.00007297419,0.0002758415,0.001201476,0.00005211466,0.0008089587],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008312846,"threshold_uncertainty_score":0.0165289,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406052391","doi":"10.1007/s41666-024-00182-5","title":"A Low Complexity Efficient Deep Learning Model for Automated Retinal Disease Diagnosis","year":2025,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Charles Darwin University","keywords":"Computer science; Optical coherence tomography; Artificial intelligence; Deep learning; Preprocessor; Transformer; Pattern recognition (psychology); Machine learning; Computer vision; Ophthalmology; Medicine; Engineering","authors":[{"name":"Sadia Sultana Chowa","is_ca":false},{"name":"Md. Rahad Islam Bhuiyan","is_ca":false},{"name":"Israt Jahan Payel","is_ca":false},{"name":"Asif Karim","is_ca":false},{"name":"Inam Ullah Khan","is_ca":false},{"name":"Sidratul Montaha","is_ca":true},{"name":"Md. Zahid Hasan","is_ca":false},{"name":"Mirjam Jonkman","is_ca":false},{"name":"Sami Azam","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1162504458299991,"gpt":0.4786765947048147,"spread":0.3624261488748155,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002672409,0.0004860304,0.0003640786,0.0003535031,0.0001654381,0.00042194,0.0008546098,0.0005751871,0.001319251],"category_scores_gemma":[0.0006980993,0.0002506043,0.0004826196,0.0002879795,0.0002160237,0.000448369,0.0004408997,0.0009341025,0.0004101385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007199126,"about_ca_system_score_gemma":0.0008845225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01236145,"about_ca_topic_score_gemma":0.01188597,"domain_scores_codex":[0.9999009,0.00001374702,0.000005557836,0.00003074407,0.00002846683,0.00002050821],"domain_scores_gemma":[0.9998784,0.00004201589,0.0000151778,0.00001127799,0.00004356488,0.000009597169],"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":[0.0001547128,0.00007306665,0.001637877,0.00004893315,0.00004890217,0.000111498,0.00002543911,0.836982,0.007997569,0.003060074,0.002916129,0.1469439],"study_design_scores_gemma":[0.000001727716,0.000008165538,0.00007342616,0.000001617652,0.000003314467,0.000009386275,0.000001162449,0.9989731,0.0004376487,0.0003581638,0.0001310848,0.00000121486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07501178,0.001201833,0.9177504,0.0005935369,0.0001058151,0.00006944045,0.0004106584,0.001867984,0.002988628],"genre_scores_gemma":[0.9091843,0.0004755631,0.08310465,0.0002478911,0.00005243164,0.0001007668,0.0007611804,0.00007014316,0.006003014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01236145,"threshold_uncertainty_score":0.02457899,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4386742622","doi":"10.1007/s41666-023-00146-1","title":"Sequence Labeling for Disambiguating Medical Abbreviations","year":2023,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Computer science; Sequence labeling; Task (project management); Natural language processing; Sequence (biology); Artificial intelligence; Information retrieval; Transformer","authors":[{"name":"Mücahit Çevik","is_ca":true},{"name":"Sanaz Mohammad Jafari","is_ca":true},{"name":"Mitchell Myers","is_ca":true},{"name":"Savaş Yıldırım","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.287230826855548,"gpt":0.5417832293555354,"spread":0.2545524024999874,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001280548,0.000957433,0.0008163378,0.007125867,0.001546581,0.001592864,0.001146851,0.001357957,0.01045254],"category_scores_gemma":[0.006331123,0.0004361683,0.001063554,0.00500279,0.0004964169,0.002707124,0.001540277,0.001274972,0.006117418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001034199,"about_ca_system_score_gemma":0.003185723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004354554,"about_ca_topic_score_gemma":0.006651388,"domain_scores_codex":[0.9987199,0.0002956629,0.0002547223,0.0003858274,0.0002620048,0.00008183419],"domain_scores_gemma":[0.9954867,0.00233132,0.0003988285,0.0005616022,0.001004172,0.0002173426],"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":[0.000754239,0.0003041484,0.006988045,0.001338873,0.0001006805,0.0009923659,0.001126776,0.006222977,0.04372959,0.03791338,0.07864776,0.8218813],"study_design_scores_gemma":[0.0002605063,0.0003921874,0.008117798,0.001266476,0.0005156574,0.00281951,0.002417675,0.3575784,0.09209921,0.1432421,0.3910836,0.0002069053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0465438,0.002222277,0.8798662,0.001745875,0.0007874435,0.0008164981,0.03357128,0.02360879,0.01083783],"genre_scores_gemma":[0.1005177,0.0006477458,0.8549631,0.0003806057,0.0001455501,0.0003427597,0.03886238,0.000705448,0.003434859],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01045254,"threshold_uncertainty_score":0.03496724,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3001553545","doi":"10.1007/s41666-019-00065-0","title":"Applying Bidirectional Transformations to the Design of Interoperable EMR Systems","year":2020,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interoperability; Computer science; Semantic interoperability; Cross-domain interoperability; Maintainability; Software engineering; Health informatics; Context (archaeology); Data exchange; Interface (matter); Modularity (biology); Health care; World Wide Web","authors":[{"name":"Jens Weber","is_ca":true},{"name":"Jeremy Ho","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1784970482041545,"gpt":0.3839130735196849,"spread":0.2054160253155304,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004600702,0.0006473035,0.0005372636,0.000780373,0.0006744868,0.002356099,0.001246497,0.001146929,0.003151181],"category_scores_gemma":[0.007121921,0.0007053854,0.001291566,0.0005269907,0.001445296,0.002256751,0.003091583,0.00147826,0.0006078024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007376482,"about_ca_system_score_gemma":0.001821576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002258908,"about_ca_topic_score_gemma":0.00297794,"domain_scores_codex":[0.9967295,0.001586627,0.0002110865,0.0003213072,0.0008193154,0.0003321272],"domain_scores_gemma":[0.9971976,0.001390813,0.0002669176,0.0006437958,0.0004311388,0.00006967125],"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":[0.0003440896,0.0002921003,0.002422565,0.0005765657,0.0001535902,0.000948758,0.001869881,0.410519,0.041643,0.3290657,0.001689595,0.2104751],"study_design_scores_gemma":[0.00008054818,0.0002369572,0.0003020077,0.0001572854,0.0001275747,0.0002461584,0.0003911018,0.7962527,0.0570904,0.1180299,0.02703396,0.00005132806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0178328,0.00007942635,0.9777085,0.0001269054,0.00002020484,0.00009664992,0.00003094782,0.001108638,0.002995777],"genre_scores_gemma":[0.4565518,0.0002653176,0.5383465,0.0001184905,0.00001232261,0.0002145579,0.0001953453,0.0006674183,0.003628323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004600702,"threshold_uncertainty_score":0.02433115,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4386828226","doi":"10.1007/s41666-023-00149-y","title":"Natural Language Processing to Classify Caregiver Strategies Supporting Participation Among Children and Youth with Craniofacial Microsomia and Other Childhood-Onset Disabilities","year":2023,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Craniofacial Disorders and Treatments","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University; McMaster University Medical Centre","funders":"National Institute on Disability, Independent Living, and Rehabilitation Research; National Institute of Dental and Craniofacial Research","keywords":"Support vector machine; Computer science; Naive Bayes classifier; Artificial intelligence; Machine learning; International Classification of Functioning, Disability and Health; Set (abstract data type); Random forest; Psychological intervention; Multinomial logistic regression; Natural language processing; Rehabilitation; Psychology; Medicine; Physical therapy","authors":[{"name":"Vera Kaelin","is_ca":false},{"name":"Andrew D. Boyd","is_ca":false},{"name":"Martha M. Werler","is_ca":false},{"name":"Natalie Parde","is_ca":false},{"name":"Mary A. Khetani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02578545027002345,"gpt":0.3817043173653624,"spread":0.3559188670953389,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001256658,0.0007453983,0.0003399064,0.002166344,0.0002659766,0.0007295844,0.0005233308,0.0005616123,0.001145596],"category_scores_gemma":[0.005005955,0.0001322841,0.0008584788,0.0008853944,0.000244967,0.0007181389,0.0004728461,0.0006455936,0.0005819718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007126704,"about_ca_system_score_gemma":0.0008373265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009437253,"about_ca_topic_score_gemma":0.01486352,"domain_scores_codex":[0.9992114,0.0003035195,0.0001026848,0.0001981658,0.0001134126,0.00007079322],"domain_scores_gemma":[0.9970734,0.002165278,0.0002631863,0.0001152861,0.0003149255,0.000067871],"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":[0.001252865,0.001380729,0.3752128,0.001545332,0.0003483833,0.002599682,0.00340273,0.02471818,0.0241497,0.001483267,0.0154571,0.5484492],"study_design_scores_gemma":[0.0001413385,0.0009495238,0.4068426,0.000441363,0.0003226772,0.003207173,0.006838133,0.5360039,0.01868264,0.006731533,0.01968267,0.0001564239],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9441025,0.001095595,0.0345271,0.0007221229,0.00007415943,0.0004186882,0.01557895,0.001120367,0.00236048],"genre_scores_gemma":[0.9223529,0.0003393999,0.0473159,0.0001589851,0.00003533553,0.0004800417,0.02830481,0.00003365719,0.0009790029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009437253,"threshold_uncertainty_score":0.01876467,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406854628","doi":"10.1007/s41666-025-00187-8","title":"Integrating the Patient Perspective into Healthcare and Real-World Evidence: The Multi-site, Cross-Disease, Patient-Centered Outcomes Research Project in the Medical Informatics Initiative (PCOR-MII)","year":2025,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Charité – Universitätsmedizin Berlin; Bundesministerium für Bildung und Forschung","keywords":"Perspective (graphical); Patient-centered outcomes; Informatics; Health care; Health informatics; Medicine; Outcomes research; Nursing; Political science; Computer science; Alternative medicine; Public health; Pathology","authors":[{"name":"Alizé A. Rogge","is_ca":false},{"name":"Rebecca Mukowski-Kickhöfel","is_ca":false},{"name":"Martin Boeker","is_ca":true},{"name":"Klemens Budde","is_ca":false},{"name":"Thomas Debertshäuser","is_ca":false},{"name":"Martin Dugas","is_ca":false},{"name":"Yeşim Erim","is_ca":false},{"name":"Hans‐Christoph Friederich","is_ca":false},{"name":"Thomas Ganslandt","is_ca":false},{"name":"Katrin Elisabeth Giel","is_ca":false},{"name":"Peter Henningsen","is_ca":false},{"name":"Tim Herrmann","is_ca":false},{"name":"Peter U. Heuschmann","is_ca":false},{"name":"Florian Junne","is_ca":false},{"name":"Oliver Kohlbacher","is_ca":false},{"name":"Andreas Kribben","is_ca":false},{"name":"Bernd Löwe","is_ca":false},{"name":"Michael Marschollek","is_ca":false},{"name":"Felix Nensa","is_ca":false},{"name":"Steffen Oeltze‐Jafra","is_ca":false},{"name":"Lars Pape","is_ca":false},{"name":"Rüdiger Pryss","is_ca":false},{"name":"Mario Schiffer","is_ca":false},{"name":"Kai M. Schmidt‐Ott","is_ca":false},{"name":"Michael Storck","is_ca":false},{"name":"Barbara Suwelack","is_ca":false},{"name":"Sylvia Thun","is_ca":false},{"name":"Frank Ückert","is_ca":false},{"name":"Julian Varghese","is_ca":false},{"name":"M. Zeier","is_ca":false},{"name":"Stephan Zipfel","is_ca":false},{"name":"Martina de Zwaan","is_ca":false},{"name":"Matthias Rose","is_ca":false},{"name":"Fabian Praßer","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.285989016265896,"gpt":0.6053786241674862,"spread":0.3193896079015902,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.3554498,0.0007491834,0.001390888,0.003517452,0.001771535,0.01060258,0.003266122,0.002752651,0.003030162],"category_scores_gemma":[0.1705183,0.0009346255,0.001722005,0.005323932,0.003629425,0.005503673,0.02057858,0.005568227,0.0008138287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004615279,"about_ca_system_score_gemma":0.02652298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003010455,"about_ca_topic_score_gemma":0.003173828,"domain_scores_codex":[0.6979334,0.2707416,0.007265727,0.007955908,0.01333266,0.002770755],"domain_scores_gemma":[0.6783618,0.1930272,0.02673081,0.05116985,0.02918917,0.02152118],"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":[0.003897215,0.006703828,0.1941677,0.007716761,0.00385439,0.001035078,0.02944966,0.01596084,0.01218567,0.1110663,0.07668103,0.5372815],"study_design_scores_gemma":[0.006168997,0.008650522,0.3766012,0.0146919,0.002847275,0.00267278,0.03450258,0.05898353,0.02816156,0.1109208,0.3549645,0.0008344062],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4561827,0.01049949,0.3198735,0.127148,0.001125569,0.02308492,0.03712188,0.002090257,0.02287371],"genre_scores_gemma":[0.3758202,0.001633675,0.575061,0.006698522,0.0005375599,0.01795657,0.02019165,0.0005968238,0.001504091],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3554498,"threshold_uncertainty_score":0.7948451,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2890576388","doi":"10.1007/s41666-019-00061-4","title":"DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders","year":2019,"lang":"en","type":"preprint","venue":"Journal of Healthcare Informatics Research","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University Health Network; Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Autoencoder; Computer science; Artificial intelligence; Anomaly detection; Convolutional neural network; Pattern recognition (psychology); Modalities; Deep learning; Anomaly (physics); Perspective (graphical); Machine learning","authors":[{"name":"Jacob Nogas","is_ca":true},{"name":"Shehroz S. Khan","is_ca":true},{"name":"Alex Mihailidis","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05296658435555367,"gpt":0.3612514232270916,"spread":0.3082848388715379,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004555412,0.001309768,0.0009658535,0.0006391673,0.0002713859,0.0006336612,0.001569329,0.0011576,0.003019974],"category_scores_gemma":[0.00102112,0.000639295,0.0007240795,0.000544745,0.0002453213,0.0008085071,0.001294864,0.001275914,0.001214108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004506587,"about_ca_system_score_gemma":0.0009840665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009835176,"about_ca_topic_score_gemma":0.02192399,"domain_scores_codex":[0.9997382,0.00002626608,0.00001050631,0.00008860043,0.00008037381,0.00005610108],"domain_scores_gemma":[0.9997389,0.00008974771,0.00003065346,0.00004253718,0.0000687716,0.00002948322],"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":[0.0006595997,0.0004637163,0.006570369,0.0001682926,0.0003071418,0.0004147087,0.00006624055,0.1308664,0.02444085,0.003063778,0.02485343,0.8081255],"study_design_scores_gemma":[0.00001265188,0.00004186087,0.0009914144,0.000009206529,0.00001571571,0.0000997817,0.000008376248,0.9925026,0.003684525,0.001780824,0.0008446269,0.00000846521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04729959,0.0008135986,0.9369935,0.0003564777,0.0003054334,0.0001020045,0.001786948,0.01060911,0.001733199],"genre_scores_gemma":[0.5508833,0.0005633485,0.4283945,0.0004165236,0.0001796652,0.0001693358,0.003834739,0.0004555783,0.01510298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009835176,"threshold_uncertainty_score":0.01955587,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4411306282","doi":"10.1007/s41666-025-00204-w","title":"LongHealth: A Question Answering Benchmark with Long Clinical Documents","year":2025,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Technische Universität München","keywords":"Benchmark (surveying); Computer science; Sorting; Identification (biology); Unstructured data; Data science; Information retrieval; Data mining; Artificial intelligence; Natural language processing; Big data; Programming language","authors":[{"name":"Lisa C. Adams","is_ca":false},{"name":"Felix Busch","is_ca":false},{"name":"Tianyu Han","is_ca":false},{"name":"Jean-Baptiste Excoffier","is_ca":false},{"name":"Matthieu Ortala","is_ca":false},{"name":"Alexander Löser","is_ca":false},{"name":"Hugo J.W.L. Aerts","is_ca":true},{"name":"Jakob Nikolas Kather","is_ca":false},{"name":"Daniel Truhn","is_ca":false},{"name":"Keno K. Bressem","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1073749660151189,"gpt":0.5006962605306308,"spread":0.3933212945155119,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00692197,0.002106709,0.0008129067,0.00240427,0.0009345267,0.002290168,0.003580946,0.003464109,0.01011655],"category_scores_gemma":[0.0388884,0.0004804884,0.001249588,0.001989087,0.0009913255,0.002802691,0.002447468,0.002016258,0.005549141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002360492,"about_ca_system_score_gemma":0.002642635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01651224,"about_ca_topic_score_gemma":0.01810037,"domain_scores_codex":[0.9928361,0.003450789,0.0007891582,0.001491123,0.001151617,0.0002811942],"domain_scores_gemma":[0.9748908,0.01863298,0.0005593622,0.002060725,0.003026236,0.0008298088],"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":[0.004711462,0.002212934,0.02027028,0.006161406,0.0007657226,0.001873295,0.001581261,0.1138426,0.01185102,0.007789371,0.4111128,0.4178279],"study_design_scores_gemma":[0.0023236,0.002708957,0.02112528,0.001227799,0.0003365053,0.002491708,0.002317381,0.6862083,0.03897753,0.02803292,0.2138954,0.0003546734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4543073,0.02524376,0.137254,0.01717604,0.003336913,0.003177011,0.2049464,0.115119,0.0394395],"genre_scores_gemma":[0.5021556,0.001677558,0.1580939,0.003874267,0.0005334038,0.001248526,0.3214742,0.002347391,0.008595166],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01651224,"threshold_uncertainty_score":0.03660733,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4399106209","doi":"10.1007/s41666-024-00167-4","title":"DDE: Deep Dynamic Epidemiological Modeling for Infectious Illness Development Forecasting in Multi-level Geographic Entities","year":2024,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"SKiN Health","funders":"","keywords":"Computer science; Epidemiology; Artificial neural network; Intervention (counseling); Econometrics; Machine learning; Geography; Data science; Artificial intelligence; Data mining; Mathematics; Medicine","authors":[{"name":"Ruhan Liu","is_ca":true},{"name":"Jiajia Li","is_ca":false},{"name":"Yang Wen","is_ca":false},{"name":"Huating Li","is_ca":false},{"name":"Ping Zhang","is_ca":false},{"name":"Bin Sheng","is_ca":false},{"name":"Dagan Feng","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.6959369516284104,"gpt":0.5676087420248161,"spread":0.1283282096035943,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001142348,0.0007677206,0.00118048,0.001087531,0.0003986098,0.001119891,0.001578058,0.001368693,0.003035835],"category_scores_gemma":[0.00523955,0.0007112314,0.001330517,0.001068173,0.0003900881,0.001432381,0.001528354,0.00198729,0.0005871335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008422385,"about_ca_system_score_gemma":0.001264492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02102492,"about_ca_topic_score_gemma":0.01849229,"domain_scores_codex":[0.9996821,0.0001106737,0.0000258304,0.00009477155,0.00004045425,0.00004625276],"domain_scores_gemma":[0.9986236,0.0009496943,0.0001154541,0.0001125325,0.000112355,0.00008628012],"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":[0.00006821765,0.00005670763,0.004755046,0.00005383758,0.0001178572,0.00006310809,0.00003737691,0.9588284,0.0002032625,0.006306202,0.003819721,0.02569025],"study_design_scores_gemma":[0.000003169298,0.000003773143,0.0001179732,0.000002783527,0.000004167779,0.000006844195,0.000003314949,0.9967782,0.00003142576,0.002809759,0.0002369043,0.000001617608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07619686,0.00169912,0.9077289,0.003414472,0.0003667505,0.00007204391,0.005905335,0.00300846,0.001608124],"genre_scores_gemma":[0.83007,0.001022221,0.1566508,0.000575438,0.0003647337,0.0002194451,0.006230068,0.0001727139,0.004694437],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02102492,"threshold_uncertainty_score":0.04180509,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4407291405","doi":"10.1007/s41666-025-00189-6","title":"A Guided Variational Autoencoder for Targeted Molecule Optimization in Drug Discovery","year":2025,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Autoencoder; Drug discovery; Drug; Medicine; Computer science; Pharmacology; Mathematics; Artificial intelligence; Bioinformatics; Biology; Artificial neural network","authors":[{"name":"Da Tan","is_ca":true},{"name":"Christopher J. Henry","is_ca":true},{"name":"Carson K. Leung","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0713148894159887,"gpt":0.451245856058421,"spread":0.3799309666424324,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001302772,0.0007971658,0.001272045,0.0004686478,0.0003903255,0.0005679265,0.001603898,0.00165939,0.001744645],"category_scores_gemma":[0.002242109,0.0007910824,0.0007709126,0.0004448432,0.0006879213,0.0007571991,0.001330784,0.001405056,0.0004220752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008053957,"about_ca_system_score_gemma":0.001678922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01261762,"about_ca_topic_score_gemma":0.01304139,"domain_scores_codex":[0.9996859,0.0001271046,0.00001900589,0.0000474754,0.00007693153,0.00004356878],"domain_scores_gemma":[0.9991911,0.0005369016,0.00004286248,0.00004840396,0.0001444477,0.00003621751],"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":[0.00005347819,0.00003302081,0.0001688763,0.00003602621,0.00004714085,0.00002680679,0.00002097878,0.9496189,0.001477579,0.00478868,0.0009011957,0.04282735],"study_design_scores_gemma":[0.000002223132,0.000006048525,0.00001051917,0.000001453985,0.000002197523,0.000002203296,8.725069e-7,0.9993207,0.000119746,0.0004650178,0.00006794403,0.000001003831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01481458,0.0004708305,0.9825852,0.000223503,0.00006871596,0.00004387717,0.00004873193,0.0003898579,0.001354628],"genre_scores_gemma":[0.5827679,0.0005232368,0.4075034,0.0005857938,0.0001274116,0.0003070178,0.0003004743,0.0002444039,0.007640387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01261762,"threshold_uncertainty_score":0.02508837,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285492222","doi":"10.1007/s41666-022-00117-y","title":"Active Learning for Multi-way Sensitivity Analysis with Application to Disease Screening Modeling","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Sensitivity (control systems); Machine learning; Computer science; Artificial intelligence; Random forest; Selection (genetic algorithm); Task (project management); Ensemble learning; Model selection; Data mining; Engineering","authors":[{"name":"Mücahit Çevik","is_ca":true},{"name":"Sabrina Angco","is_ca":true},{"name":"Elham Heydarigharaei","is_ca":true},{"name":"Hadi Jahanshahi","is_ca":true},{"name":"Nicholas Prayogo","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0927543417513914,"gpt":0.4284659217935231,"spread":0.3357115800421317,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0136471,0.001927262,0.003828804,0.002051989,0.001113266,0.002410913,0.003385501,0.002837851,0.003536372],"category_scores_gemma":[0.03302116,0.001753497,0.003947838,0.001324385,0.001616332,0.002048507,0.003314897,0.004991687,0.0004579978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00123428,"about_ca_system_score_gemma":0.001771721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00671254,"about_ca_topic_score_gemma":0.005966875,"domain_scores_codex":[0.9954907,0.003214031,0.0002080606,0.0004699659,0.0004468364,0.0001704656],"domain_scores_gemma":[0.94516,0.05136293,0.0007921239,0.001220545,0.001187146,0.0002772744],"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":[0.0001431743,0.0001589781,0.0008185891,0.0001482729,0.0003426007,0.000108351,0.0001012751,0.9475417,0.0008430792,0.01461848,0.0005661969,0.0346093],"study_design_scores_gemma":[0.00000624125,0.00001481118,0.00004149851,0.000003847339,0.00001260188,0.0000101655,0.000002661834,0.9950829,0.0001507729,0.00454923,0.0001195255,0.000005753439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004103031,0.0001916707,0.9949503,0.0001071902,0.00002525406,0.00003812134,0.0000544043,0.0002950485,0.0002349267],"genre_scores_gemma":[0.4704115,0.0005234614,0.5227284,0.0002974804,0.0001651739,0.0007969318,0.0004662405,0.000443638,0.004167063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0136471,"threshold_uncertainty_score":0.0721736,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410997302","doi":"10.1007/s41666-025-00201-z","title":"Novel Videographic-Free Framework for Tracking Anatomical Structures Using Swallowing Accelerometer Signals and Multi-task Transformers","year":2025,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"North York General Hospital; University of Toronto","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Natural Sciences and Engineering Research Council of Canada","keywords":"Accelerometer; Computer science; Transformer; Swallowing; Tracking (education); Computer vision; Task (project management); Artificial intelligence; Medicine; Engineering; Psychology; Electrical engineering; Voltage; Dentistry","authors":[{"name":"Ayman Anwar","is_ca":true},{"name":"W. Li","is_ca":true},{"name":"Amanda S. Mahoney","is_ca":false},{"name":"Yassin Khalifa","is_ca":false},{"name":"James L. Coyle","is_ca":false},{"name":"Ervin Sejdić","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1675499289808574,"gpt":0.4838603174357275,"spread":0.3163103884548701,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031157,0.001049539,0.0009639945,0.0009549705,0.0002387711,0.0007060643,0.001091413,0.001066747,0.002935069],"category_scores_gemma":[0.0008285705,0.0004044638,0.0008015778,0.0008313852,0.0001687975,0.0006526787,0.001246082,0.0007101582,0.001951378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001760151,"about_ca_system_score_gemma":0.0006270595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003648683,"about_ca_topic_score_gemma":0.009320543,"domain_scores_codex":[0.9997514,0.00002894097,0.00001442973,0.00007575231,0.00008901822,0.00004049114],"domain_scores_gemma":[0.9998179,0.00003279825,0.0000219573,0.00002556256,0.00007752726,0.00002436221],"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":[0.0005626269,0.0001967338,0.002117262,0.0002570483,0.0001884185,0.0003950103,0.0001029537,0.0260385,0.1095652,0.001717012,0.008676352,0.850183],"study_design_scores_gemma":[0.00005521981,0.0002280746,0.005855361,0.00004194744,0.0001107935,0.001050758,0.00006908132,0.9525201,0.02767693,0.003500024,0.008843903,0.00004785954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006289361,0.0003550532,0.9902475,0.00005868275,0.00007922704,0.00005361301,0.0003014658,0.001886928,0.0007279901],"genre_scores_gemma":[0.2265218,0.001147827,0.7583253,0.0003738879,0.0002777749,0.0003591907,0.002527019,0.0005014745,0.009965677],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003648683,"threshold_uncertainty_score":0.009818852,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}