{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":366,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":366,"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"},"query_hash":"da1b422238c4","filters":{"venue":"Medical Image Analysis"}},"results":[{"id":"W1884191083","doi":"10.1016/j.media.2016.05.004","title":"Brain tumor segmentation with Deep Neural Networks","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":3245,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Computer science; Convolutional neural network; Exploit; Artificial intelligence; Segmentation; Deep learning; Set (abstract data type); Deep neural networks; Pattern recognition (psychology); Layer (electronics); Machine learning; Architecture; Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.01320400802125175,"gpt":0.2671777228647449,"spread":0.2539737148434931,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003020263,0.0001145367,0.0001724657,0.0001795749,0.0001288861,0.00006121757,0.0002061171,0.00004260697,0.00295615],"category_scores_gemma":[0.001136206,0.00006546754,0.0001138972,0.001389303,0.00025706,0.0002458902,0.0000280901,0.0001256179,0.0000920373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004451404,"about_ca_system_score_gemma":0.00001759588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001614603,"about_ca_topic_score_gemma":0.0001207087,"domain_scores_codex":[0.9982326,0.0002476258,0.0002315972,0.0004084073,0.000643035,0.0002367389],"domain_scores_gemma":[0.9989243,0.0004243891,0.0001171746,0.0002645475,0.00004291727,0.0002267141],"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":[0.0002026437,0.0002928001,0.008634997,0.00001271281,0.0002548087,0.000514108,0.0002346548,0.0005291236,0.4716101,0.00048886,0.00261392,0.5146112],"study_design_scores_gemma":[0.001305905,0.0001319548,0.01370391,0.00001357522,0.0004052348,0.00008861911,0.0001473809,0.9040349,0.07895698,0.00007929927,0.0008163942,0.0003159094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1513045,0.000009087407,0.8325507,0.01515305,0.00007738709,0.0000981445,0.000002461147,0.0001422579,0.0006623492],"genre_scores_gemma":[0.9927716,0.000007858063,0.000231734,0.006056994,0.0001026156,0.00003054416,0.00000480726,0.00001217167,0.000781674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9035057,"threshold_uncertainty_score":0.9979553,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2890139949","doi":"10.1016/j.media.2019.101552","title":"Generative adversarial network in medical imaging: A review","year":2019,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":1852,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Philips (Canada); University of Saskatchewan","funders":"","keywords":"Adversarial system; Computer science; Discriminator; Artificial intelligence; Generative grammar; Consistency (knowledge bases); Machine learning; Image translation; Segmentation; Domain (mathematical analysis); Image (mathematics); Translation (biology); Adaptation (eye); Medical imaging; Deep learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02797709482192573,"gpt":0.368006182483292,"spread":0.3400290876613663,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004195503,0.0006119173,0.004240989,0.0007069933,0.00008343539,0.0001454832,0.003356359,0.000570989,0.00325091],"category_scores_gemma":[0.00181853,0.0004730886,0.001896675,0.008316054,0.0002124173,0.000422183,0.001123729,0.001764854,0.000617102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000516352,"about_ca_system_score_gemma":0.002313426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001649746,"about_ca_topic_score_gemma":0.0002105957,"domain_scores_codex":[0.9913112,0.001597119,0.001749122,0.001477056,0.003118228,0.0007472956],"domain_scores_gemma":[0.9961624,0.0007467376,0.0006832541,0.00165997,0.0001512038,0.0005964151],"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":[8.244154e-7,0.00003437329,0.000006664361,0.005023484,0.000809771,0.0005303533,0.00001511546,0.000004298274,3.997657e-9,0.00008286304,0.02434421,0.969148],"study_design_scores_gemma":[0.0002048579,0.00001415848,0.000001718492,0.02352684,0.004485098,0.00007185792,0.000001102483,0.05479938,5.026095e-8,0.00006791797,0.9163786,0.0004483876],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[6.538938e-9,0.6523762,0.343695,0.001900369,0.0007967545,0.0003939196,0.000003524939,0.00007762618,0.0007565611],"genre_scores_gemma":[3.936164e-7,0.9897051,0.00470129,0.003613498,0.001443235,0.0001888014,0.00009898753,0.00003510626,0.0002135535],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9686996,"threshold_uncertainty_score":0.9997721,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2910094941","doi":"10.1016/j.media.2022.102680","title":"The Liver Tumor Segmentation Benchmark (LiTS)","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1164,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"National Cancer Institute; National Institutes of Health; Fonds de Recherche du Québec - Santé; Fondation de l'Association des radiologistes du Québec; International Graduate School of Science and Engineering; Universität Zürich; Deutsche Forschungsgemeinschaft","keywords":"Benchmark (surveying); Segmentation; Computer science; Artificial intelligence; Medical physics; Medicine; Pattern recognition (psychology); Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.007724142912540659,"gpt":0.2656009642944292,"spread":0.2578768213818886,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005617029,0.00007964259,0.0001211712,0.00009148031,0.0009141251,0.00009444047,0.001253837,0.00001157418,0.001010731],"category_scores_gemma":[0.0001202665,0.00005859865,0.0001434654,0.002639343,0.00009728655,0.0002100285,0.0006511899,0.0002380598,0.00006076638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006536023,"about_ca_system_score_gemma":0.00004546697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003417503,"about_ca_topic_score_gemma":0.00004581247,"domain_scores_codex":[0.9981198,0.0001929051,0.0002288449,0.0003216941,0.0009128981,0.0002237994],"domain_scores_gemma":[0.9987161,0.0004054899,0.0001013113,0.0005944103,0.0000484159,0.000134255],"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":[0.00001739301,0.0003986204,0.001981643,0.000008671672,0.001095283,0.0005054162,0.001198138,0.009216052,0.001413884,0.04580027,0.06759614,0.8707685],"study_design_scores_gemma":[0.0001622089,0.00003002764,0.002173926,0.000001013879,0.000196667,0.00002095455,0.000145062,0.9451938,0.0003137721,0.00284923,0.04875661,0.0001567828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003332629,0.0004221789,0.9870038,0.008218525,0.00008523368,0.0001253658,0.000003524128,0.00009734214,0.0007114176],"genre_scores_gemma":[0.9018634,0.000432051,0.08322024,0.01009703,0.0002910784,0.0009539301,0.0001125765,0.00002227448,0.003007416],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9359777,"threshold_uncertainty_score":0.9999025,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2106033751","doi":"10.1016/j.media.2013.12.002","title":"Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge","year":2013,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":820,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Robarts Clinical Trials","funders":"National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute; KWF Kankerbestrijding; National Institutes of Health; National Science Foundation","keywords":"Artificial intelligence; Segmentation; Computer science; Algorithm; Computer vision; Prostate; Pattern recognition (psychology); Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.03657965750292922,"gpt":0.3547086912706948,"spread":0.3181290337677656,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005184345,0.0001302823,0.0002695637,0.0002232306,0.0001039622,0.0001226963,0.000812546,0.00007414351,0.001290524],"category_scores_gemma":[0.001319772,0.00008410465,0.0002076533,0.001065299,0.0001956136,0.0007082358,0.0001351513,0.0001249831,0.00003285795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007019517,"about_ca_system_score_gemma":0.0001905723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001348675,"about_ca_topic_score_gemma":0.0000201749,"domain_scores_codex":[0.9956735,0.0004506154,0.0005555052,0.0003684741,0.002715063,0.0002368092],"domain_scores_gemma":[0.9973446,0.000293991,0.0002737311,0.0005536917,0.001359279,0.0001747046],"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":[0.000002002142,0.0001659828,0.00007275564,0.00002989802,0.000523212,0.000001227024,0.001154323,0.00001912617,0.002134498,0.0001447379,0.004549483,0.9912028],"study_design_scores_gemma":[0.000748804,0.0001060501,0.0007875832,0.00001723732,0.0009898231,0.00000118659,0.0002257401,0.9520553,0.03980379,0.005059289,0.00007665533,0.0001285212],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001550177,0.0001524081,0.9877267,0.00855152,0.00006116697,0.001505125,0.000004540532,0.00009571078,0.0003526637],"genre_scores_gemma":[0.1801832,0.0002980967,0.812542,0.001710753,0.0001847405,0.004515063,0.00014772,0.00002638919,0.0003919874],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9910742,"threshold_uncertainty_score":0.9996224,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3089090082","doi":"10.1016/j.media.2020.101813","title":"Deep neural network models for computational histopathology: A survey","year":2020,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":727,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"National Cancer Institute; Canadian Cancer Society","keywords":"Artificial intelligence; Artificial neural network; Computer science; Deep learning; Pattern recognition (psychology); Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.0621987312965477,"gpt":0.3509627319969386,"spread":0.2887640007003909,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00166656,0.0004026707,0.002288199,0.0003464367,0.0001790063,0.0001552956,0.001803641,0.0003847402,0.00009212264],"category_scores_gemma":[0.0005963183,0.0003485289,0.001436633,0.003700943,0.000157645,0.0002930166,0.0004708187,0.0006069004,0.00004527379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002685597,"about_ca_system_score_gemma":0.0004882027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006638058,"about_ca_topic_score_gemma":0.0001816056,"domain_scores_codex":[0.99567,0.0009309958,0.0009276632,0.001085649,0.0009219204,0.0004637572],"domain_scores_gemma":[0.9966986,0.001537223,0.0005034455,0.0006369815,0.0002484003,0.0003753242],"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":[0.000002859138,0.00001956075,0.000004191076,0.0005324639,0.0006191214,0.00008623459,0.00002438275,0.0312806,2.169676e-9,0.0002138272,0.003032302,0.9641845],"study_design_scores_gemma":[0.00009106142,0.00003469914,0.00001121901,0.0001138958,0.001984874,0.00002890427,4.077195e-7,0.7912154,5.082457e-9,0.00126926,0.2050026,0.0002477387],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[2.695444e-8,0.4681278,0.5310004,0.0002661695,0.0002693466,0.0001942469,0.00001887389,0.00009284675,0.00003029104],"genre_scores_gemma":[0.00002057952,0.8949793,0.1022098,0.0008832692,0.000773444,0.0002854836,0.0007435356,0.00004577174,0.00005884251],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9639367,"threshold_uncertainty_score":0.9998966,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3199008037","doi":"10.1016/j.media.2021.102233","title":"BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":708,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"National Institute of Neurological Disorders and Stroke; National Institutes of Health","keywords":"Computer science; Functional magnetic resonance imaging; Artificial intelligence; Pooling; Connectome; Neuroimaging; Pattern recognition (psychology); Graph; Human Connectome Project; Machine learning; Psychology; Neuroscience; Functional connectivity; Theoretical computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.02012230046150534,"gpt":0.2959105910532257,"spread":0.2757882905917204,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001241182,0.0002938087,0.0009827571,0.0006126829,0.0004741643,0.0001989326,0.0004841702,0.0001370926,0.002603463],"category_scores_gemma":[0.05616597,0.0002688113,0.00177201,0.01114808,0.0003717267,0.0003010893,0.0003526558,0.0003558862,0.00003911988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006100631,"about_ca_system_score_gemma":0.0001148588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009297395,"about_ca_topic_score_gemma":0.001323137,"domain_scores_codex":[0.9958013,0.0005616191,0.0005536958,0.001238818,0.001123221,0.0007213054],"domain_scores_gemma":[0.9836333,0.01490008,0.0001657371,0.0006977133,0.0002654906,0.0003376178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005272626,0.001739943,0.0962441,0.0002409519,0.05586958,0.002793215,0.002036291,0.06890212,0.0515641,0.005810818,0.6794989,0.03477273],"study_design_scores_gemma":[0.001484148,0.0001856453,0.01475353,0.000034889,0.01983927,0.00004672687,0.0004803817,0.908331,0.01373487,0.0056798,0.03429899,0.001130776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08861177,0.0007127791,0.7372741,0.1683194,0.0007783654,0.0003548435,0.0001546394,0.0003694533,0.003424735],"genre_scores_gemma":[0.9451094,0.00005397102,0.00293561,0.04787698,0.0005382937,0.000116556,0.000120034,0.00003423221,0.003214935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8564976,"threshold_uncertainty_score":0.9999764,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4377695098","doi":"10.1016/j.media.2023.102846","title":"Diffusion models in medical imaging: A comprehensive survey","year":2023,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":652,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Probabilistic logic; Diffusion map; Noise (video); Noise reduction; Machine learning; Diffusion; Medical imaging; Data science; Image (mathematics); Nonlinear dimensionality reduction","retraction":null,"screen_n_in":null,"score":{"opus":0.08373872611063253,"gpt":0.387150647904169,"spread":0.3034119217935364,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003297213,0.0005913317,0.003228669,0.002203053,0.0001080169,0.0002214618,0.003472345,0.0006632756,0.0004940628],"category_scores_gemma":[0.001659997,0.0004763559,0.001236262,0.01188197,0.0002866267,0.0004545509,0.001942567,0.001631543,0.0004028256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004325724,"about_ca_system_score_gemma":0.001205198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003242386,"about_ca_topic_score_gemma":0.00295645,"domain_scores_codex":[0.9905379,0.001910442,0.001601945,0.001521752,0.003715965,0.0007120034],"domain_scores_gemma":[0.9948472,0.002174457,0.0004716296,0.001518241,0.0002511939,0.0007373176],"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":[0.00000148018,0.00007394154,0.00004538329,0.001165084,0.0006461053,0.001185099,0.00005522802,0.0000152684,3.138461e-8,0.00003683847,0.001879519,0.994896],"study_design_scores_gemma":[0.0003074494,0.00001458255,0.0002176906,0.004313667,0.001376817,0.00006437815,0.000009971498,0.8360782,1.16102e-7,0.0004814734,0.1564875,0.0006481431],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000002155632,0.5724848,0.4262063,0.0003576182,0.0003460863,0.0002158664,0.00001631005,0.0002345382,0.0001363112],"genre_scores_gemma":[0.00002755738,0.9979584,0.0008449522,0.0003807022,0.0002044413,0.000133295,0.0002245095,0.00005758628,0.0001685632],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9942479,"threshold_uncertainty_score":0.9997688,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3136424010","doi":"10.1016/j.media.2021.102035","title":"Loss odyssey in medical image segmentation","year":2021,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":618,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Sunnybrook Health Science Centre; University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Segmentation; Computer science; Dice; Artificial intelligence; Function (biology); Hausdorff distance; Loss function; Boundary (topology); Benchmark (surveying); Image segmentation; Mathematics; Geography; Statistics; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.02602045313479882,"gpt":0.3855040646034736,"spread":0.3594836114686747,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001236884,0.0004948057,0.002434032,0.000806583,0.0001117747,0.0002114846,0.002791936,0.000531085,0.002177654],"category_scores_gemma":[0.001212124,0.0004143536,0.00112061,0.009672324,0.0002824693,0.0005088295,0.001062869,0.001270043,0.0003690562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002315676,"about_ca_system_score_gemma":0.0008025012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000446269,"about_ca_topic_score_gemma":0.0001825909,"domain_scores_codex":[0.9934042,0.0007470667,0.001468868,0.001363606,0.002411006,0.0006052745],"domain_scores_gemma":[0.9961274,0.00103272,0.0004495098,0.001519711,0.0001550136,0.000715608],"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":[2.674819e-7,0.0001280042,0.000007652737,0.0004714188,0.0004279803,0.001760237,0.00002805139,0.000004435325,4.93812e-7,0.0005421234,0.001356381,0.9952729],"study_design_scores_gemma":[0.0004190932,0.00002077861,0.0000170423,0.003700453,0.00326897,0.0002476946,0.00001861339,0.04441783,0.000008077558,0.0004435167,0.9464445,0.0009934835],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[7.202627e-7,0.5075225,0.4907232,0.001135619,0.00007393076,0.0002013767,0.00000675782,0.00008444151,0.000251488],"genre_scores_gemma":[0.000004535128,0.9583774,0.03961429,0.0006668722,0.0002562257,0.0002971145,0.0005281306,0.00003271284,0.0002226966],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9942794,"threshold_uncertainty_score":0.9998308,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2484736472","doi":"10.1016/j.media.2016.07.009","title":"ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":531,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; Université de Sherbrooke","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; National Cancer Institute; Northeastern University; National Institute for Health and Care Research","keywords":"Segmentation; Benchmarking; Computer science; Artificial intelligence; Stroke (engine); Multispectral image; Benchmark (surveying); Magnetic resonance imaging; Lesion; Medicine; Pattern recognition (psychology); Machine learning; Radiology; Pathology; Cartography; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.02759396875597288,"gpt":0.3462863085249213,"spread":0.3186923397689485,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001205714,0.000232852,0.0005190162,0.0004450266,0.00008627283,0.00004635809,0.0002237456,0.0001752261,0.008741614],"category_scores_gemma":[0.001911113,0.0001564321,0.0004546986,0.0005545886,0.0001460765,0.0003148583,0.0001065325,0.000139612,0.000107533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000376962,"about_ca_system_score_gemma":0.000169149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001559462,"about_ca_topic_score_gemma":0.0001237958,"domain_scores_codex":[0.9964902,0.000109847,0.0005628751,0.0006250676,0.001803088,0.0004089644],"domain_scores_gemma":[0.9981435,0.0003570789,0.0002154384,0.0005213507,0.0003821511,0.0003804578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002065605,0.0003630299,0.02621443,0.00003719957,0.004820996,0.00003460583,0.000182455,0.000002116599,0.3534071,0.00001129755,0.4104241,0.2042961],"study_design_scores_gemma":[0.03857743,0.0008453264,0.08793711,0.000590168,0.05980694,0.00002566942,0.002303605,0.3071328,0.3659664,0.000257759,0.1348903,0.001666472],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3810941,0.0003865813,0.5856303,0.02768803,0.0001555629,0.000991437,0.0001569969,0.0001080483,0.003788909],"genre_scores_gemma":[0.9555487,0.0003221962,0.03707002,0.0009768265,0.0006024701,0.00033827,0.00220365,0.00003476291,0.002903102],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5744545,"threshold_uncertainty_score":0.9921646,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2966434031","doi":"10.1016/j.media.2020.101851","title":"Boundary loss for highly unbalanced segmentation","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":459,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Boundary (topology); Computer science; Artificial intelligence; Computer vision; Mathematical optimization; Mathematics; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.03034175931016476,"gpt":0.3062704956848611,"spread":0.2759287363746963,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001845682,0.00008511109,0.0001776375,0.0000893734,0.0001533093,0.00008418899,0.0001959331,0.00005206124,0.001069214],"category_scores_gemma":[0.002026368,0.00007577014,0.0001820529,0.001283885,0.0001541366,0.0001593131,0.00002375368,0.0001146349,0.0001402979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002978056,"about_ca_system_score_gemma":0.00005015378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005631757,"about_ca_topic_score_gemma":0.000005962127,"domain_scores_codex":[0.9986355,0.00009643711,0.0002402218,0.0003736031,0.0004939159,0.0001602989],"domain_scores_gemma":[0.9992869,0.0001896466,0.00009219052,0.0001428219,0.00004686193,0.0002415609],"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":[0.00009967343,0.0001037518,0.0006206273,0.00004122585,0.0001260033,0.00004259856,0.0004250746,0.00002999958,0.9554171,0.0005354736,0.006527483,0.03603102],"study_design_scores_gemma":[0.001384337,0.0001349299,0.003609169,0.00000579981,0.0006087003,0.000007164625,0.0002351517,0.2961838,0.671142,0.0004462423,0.0259622,0.0002804757],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1143528,0.00001929646,0.8339949,0.04989741,0.0001469142,0.0002434999,0.00003460337,0.0002335828,0.001077042],"genre_scores_gemma":[0.9854594,0.0000233868,0.0008978408,0.01302909,0.0001634491,0.00005289902,0.00004072272,0.000009844254,0.0003233088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8711067,"threshold_uncertainty_score":0.999844,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2118891573","doi":"10.1016/s1361-8415(00)00008-6","title":"T-snakes: Topology adaptive snakes","year":2000,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":413,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Topology (electrical circuits); Computer science; Artificial intelligence; Mathematics; Combinatorics","retraction":null,"screen_n_in":null,"score":{"opus":0.004922164412723464,"gpt":0.2713293990795815,"spread":0.266407234666858,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004594132,0.0002383093,0.0004739404,0.000225373,0.0001003862,0.00004038702,0.0005124076,0.0003055852,0.01964811],"category_scores_gemma":[0.0002969619,0.0002084204,0.0006302285,0.0008781306,0.0004393115,0.000009977587,0.0001074366,0.0002104236,0.0002355715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001829977,"about_ca_system_score_gemma":0.00007393814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003016647,"about_ca_topic_score_gemma":0.0005879514,"domain_scores_codex":[0.9978585,0.0001945598,0.0004157651,0.0006594756,0.0004701832,0.0004015336],"domain_scores_gemma":[0.9986744,0.00003476612,0.00007954506,0.0007947583,0.0001347688,0.0002817671],"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":[0.0005487401,0.00168329,0.01901084,0.00004020334,0.01969182,0.001532427,0.0003513683,0.0001469668,0.2795483,0.0002401864,0.2652521,0.4119538],"study_design_scores_gemma":[0.001774194,0.001118661,0.007388419,0.00003143878,0.008944613,0.0001240368,0.0005535565,0.01479223,0.4575751,0.0006419424,0.5050712,0.001984623],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7312986,0.002200843,0.1331824,0.002489623,0.00004135327,0.0003040686,0.00002335005,0.0002426918,0.1302171],"genre_scores_gemma":[0.9772775,0.001203105,0.004596874,0.002533779,0.0003686576,0.00004183679,0.0004242794,0.00003167405,0.01352231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4099692,"threshold_uncertainty_score":0.9812481,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2134834581","doi":"10.1016/s1361-8415(00)00014-1","title":"An algorithmic overview of surface registration techniques for medical imaging","year":2000,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":411,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Robarts Clinical Trials; McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Computer science; Rigid transformation; Robustness (evolution); Image registration; Representation (politics); Artificial intelligence; Modalities; Transformation (genetics); Surface (topology); Similarity (geometry); Exploit; Similarity measure; Matching (statistics); Point set registration; Computer vision; Theoretical computer science; Point (geometry); Mathematics; Image (mathematics); Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.01076959209935504,"gpt":0.3015921323986959,"spread":0.2908225402993409,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001071094,0.0001620958,0.0005021388,0.0001826917,0.00005339706,0.00003689204,0.0003395529,0.0001377584,0.004223445],"category_scores_gemma":[0.0001514743,0.0001466336,0.0003922372,0.0008796182,0.0001002052,0.0001862908,0.00001066702,0.0001802736,0.00001107871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002930249,"about_ca_system_score_gemma":0.00004899081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002382802,"about_ca_topic_score_gemma":0.000085084,"domain_scores_codex":[0.9980081,0.00006134029,0.0005826818,0.0002661475,0.0008350189,0.0002466574],"domain_scores_gemma":[0.9991005,0.00008647858,0.00005014314,0.0003811693,0.0001079367,0.0002737781],"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":[0.00001308432,0.0002095139,0.0009617889,0.0003148477,0.001647069,0.00006023463,0.0001950027,0.02309057,0.002462697,0.00003720647,0.003253897,0.9677541],"study_design_scores_gemma":[0.0001396946,0.00001348007,0.00006675186,0.00005914619,0.0008950845,0.000003401509,0.00003003405,0.995624,0.002126081,0.0001202212,0.0007663394,0.0001557549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03326503,0.001418161,0.9632478,0.0007081867,0.00002084905,0.00008731087,0.00002399235,0.0003414058,0.0008873094],"genre_scores_gemma":[0.9769732,0.002043931,0.02025737,0.0001622516,0.0001497796,0.00001777073,0.0001985153,0.00003181555,0.0001653881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9725335,"threshold_uncertainty_score":0.9966868,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2150903265","doi":"10.1016/j.media.2013.05.008","title":"Medical image processing on the GPU – Past, present and future","year":2013,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":405,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Vetenskapsrådet","keywords":"Computer science; Image processing; Graphics processing unit; Medical imaging; Artificial intelligence; Computer vision; Histogram; General-purpose computing on graphics processing units; Interpolation (computer graphics); Graphics; Computer graphics; Image registration; Computer graphics (images); Image (mathematics); Parallel computing","retraction":null,"screen_n_in":null,"score":{"opus":0.03209868690587763,"gpt":0.3864913773342081,"spread":0.3543926904283305,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001766116,0.0006675932,0.002761077,0.0004668447,0.0002756138,0.0001971141,0.0008903358,0.0008700231,0.0138912],"category_scores_gemma":[0.0009361858,0.0003299824,0.001138851,0.002030048,0.001174915,0.00009530483,0.0004246667,0.00255981,0.0003035779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008595035,"about_ca_system_score_gemma":0.0006669224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006614589,"about_ca_topic_score_gemma":0.000002522216,"domain_scores_codex":[0.9937102,0.0003877532,0.001217419,0.0009950493,0.003074428,0.0006151573],"domain_scores_gemma":[0.9956487,0.0007579516,0.0004300557,0.001300755,0.0002428064,0.001619744],"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":[0.000002212818,0.0001943012,0.000003894505,0.002731241,0.0008384433,0.0002107743,0.00002080366,1.830255e-9,7.33156e-7,0.00009593969,0.1944552,0.8014465],"study_design_scores_gemma":[0.0002016631,0.00004774392,0.00001044442,0.004995549,0.01056416,0.0001975089,0.000041442,0.002495453,0.000002452165,0.00005855529,0.9810753,0.0003097476],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000006002646,0.8201616,0.007655297,0.1667378,0.00005644267,0.001362471,0.00002220953,0.0002580944,0.003740065],"genre_scores_gemma":[0.000007772175,0.9832816,0.004707167,0.003833929,0.005459144,0.0009496653,0.0003634175,0.00008386511,0.001313506],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8011367,"threshold_uncertainty_score":0.9999152,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2021204548","doi":"10.1016/j.media.2012.09.004","title":"Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging","year":2012,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":361,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Segmentation; Magnetic resonance imaging; Multiple sclerosis; Computer science; Artificial intelligence; Lesion; Image segmentation; Pattern recognition (psychology); Medicine; Radiology; Pathology","retraction":null,"screen_n_in":null,"score":{"opus":0.130202479353036,"gpt":0.4478487463707702,"spread":0.3176462670177342,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003620278,0.0004969876,0.004500386,0.001082648,0.0000857246,0.00001423066,0.0004084533,0.0001892059,0.0200858],"category_scores_gemma":[0.004261789,0.000354218,0.002366328,0.002522592,0.0006215023,0.0001087249,0.0002894812,0.0006295834,0.0001831795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001826791,"about_ca_system_score_gemma":0.0002933565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006680618,"about_ca_topic_score_gemma":0.00000732392,"domain_scores_codex":[0.992822,0.001703059,0.002297716,0.0005965073,0.002083006,0.0004977022],"domain_scores_gemma":[0.9951417,0.001928969,0.001007274,0.0009440706,0.0005469706,0.0004309988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000006669074,0.0003491657,0.003058843,0.1306936,0.001182929,0.000006070401,0.00002819902,5.982118e-8,0.00002409249,0.000001055624,0.004755515,0.8598938],"study_design_scores_gemma":[0.001597549,0.0001541251,0.04960625,0.5634264,0.0647881,0.00002441398,0.00006643233,0.002803223,0.00006105946,0.000003021574,0.316827,0.0006424405],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000005325012,0.9930235,0.004451665,0.0006411886,0.00006530002,0.001178253,0.0001807366,0.00002888864,0.0004252138],"genre_scores_gemma":[0.00002762937,0.9636894,0.03428182,0.0005744564,0.0001008918,0.0003587224,0.0005867062,0.0000532374,0.0003271308],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8592514,"threshold_uncertainty_score":0.999891,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2029098063","doi":"10.1016/j.media.2007.12.003","title":"Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI","year":2008,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":360,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Segmentation; Active appearance model; Artificial intelligence; Computer science; Gauss; Pattern recognition (psychology); Statistical model; Computer vision; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.02068048989772678,"gpt":0.3010764742899867,"spread":0.2803959843922599,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008744074,0.0001340734,0.0008448756,0.0006070703,0.00007316531,0.00002353888,0.0003475857,0.00008082969,0.0001031405],"category_scores_gemma":[0.0003619972,0.0001136577,0.0002493128,0.00218862,0.0006326818,0.0001276276,0.0001872714,0.00008713498,4.416935e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001391494,"about_ca_system_score_gemma":0.00006548678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001838254,"about_ca_topic_score_gemma":0.000008089553,"domain_scores_codex":[0.9976905,0.0001351428,0.0005887828,0.0004576484,0.0009007444,0.000227178],"domain_scores_gemma":[0.9986044,0.0002741349,0.0001809158,0.0003991322,0.0002398415,0.000301611],"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":[0.0003174279,0.004019504,0.05966026,0.00247341,0.07786955,0.0004062035,0.01705937,0.06997238,0.06103812,0.04985915,0.01965121,0.6376734],"study_design_scores_gemma":[0.000185977,0.00004955878,0.002904385,0.00001155654,0.00196992,0.000001504936,0.00002014689,0.9899225,0.004545392,0.000268042,0.00001468489,0.0001063428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03515038,0.0008378662,0.9634792,0.0002202071,0.0000151706,0.0001443248,0.00005922414,0.0000368748,0.00005671051],"genre_scores_gemma":[0.4829625,0.0007272988,0.5159617,0.0002188563,0.00001459795,0.00003040508,0.00003425925,0.000006943397,0.00004345773],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9199501,"threshold_uncertainty_score":0.4634826,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2580480204","doi":"10.1016/j.media.2017.01.009","title":"A deep learning approach for the analysis of masses in mammograms with minimal user intervention","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":342,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Segmentation; Computer science; Deep learning; Pattern recognition (psychology); Classifier (UML); Mammography; Bayesian probability; Visualization; Test set; Machine learning; Breast cancer","retraction":null,"screen_n_in":null,"score":{"opus":0.01522990514961771,"gpt":0.2996650691174222,"spread":0.2844351639678045,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001383239,0.0001118433,0.0004153567,0.0005542514,0.0002215825,0.0002467833,0.001244996,0.00007220101,0.00009142111],"category_scores_gemma":[0.0005245175,0.00007164352,0.0005061179,0.00215417,0.0002310494,0.0004207476,0.000222334,0.0002079539,8.86334e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004626481,"about_ca_system_score_gemma":0.00002610645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001011953,"about_ca_topic_score_gemma":0.002438138,"domain_scores_codex":[0.9983015,0.0001256223,0.000347569,0.0003919894,0.000620665,0.0002126643],"domain_scores_gemma":[0.9983851,0.0002393532,0.0003787386,0.0008012974,0.0001355332,0.00006001044],"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":[0.0001131957,0.0003548292,0.2773192,0.00007582446,0.01305734,0.00002414859,0.001030628,0.03178151,0.0000806455,0.0001955004,0.00003838651,0.6759288],"study_design_scores_gemma":[0.0003227363,0.00006774389,0.1108156,0.00001056731,0.003110214,9.609045e-7,0.0001869159,0.8851016,0.0001352697,0.0000173842,0.0001509269,0.00008012694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03008858,0.0001277033,0.9689425,0.0004941961,0.00002806774,0.0001156323,8.670978e-7,0.00002463617,0.0001778751],"genre_scores_gemma":[0.9486552,0.00002620026,0.05104491,0.0000294543,0.00002563145,0.00008480731,0.00001172613,0.000006048583,0.0001159688],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9185666,"threshold_uncertainty_score":0.2921538,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2132322720","doi":"10.1016/s1361-8415(03)00037-9","title":"A fully automatic and robust brain MRI tissue classification method","year":2003,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":336,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Parametric statistics; Classifier (UML); Data set; Set (abstract data type); Magnetic resonance imaging; Mathematics; Statistics; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.01855722862763469,"gpt":0.3401510110432345,"spread":0.3215937824155998,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002850471,0.0001749271,0.0003995671,0.0004463965,0.000120469,0.0002459365,0.0006474261,0.0001387357,0.001937795],"category_scores_gemma":[0.003205334,0.0001488885,0.0001152465,0.002175478,0.0001932351,0.0005202226,0.0001329276,0.0002483608,0.00007162368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004866011,"about_ca_system_score_gemma":0.0001197403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005179056,"about_ca_topic_score_gemma":0.00002169704,"domain_scores_codex":[0.9966839,0.0007893838,0.0005276833,0.0005960699,0.001100395,0.0003025743],"domain_scores_gemma":[0.997906,0.0006150986,0.0001660324,0.0006806951,0.000131657,0.000500511],"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":[0.000001357883,0.0002103451,0.0004686954,0.00006572242,0.0005132916,0.0001779453,0.0007045547,0.00001314389,0.009004286,0.00701326,0.02505904,0.9567683],"study_design_scores_gemma":[0.0004562471,0.00007507628,0.002418882,0.00002923261,0.0004415053,0.00006009806,0.000136181,0.9687893,0.02188301,0.002372942,0.002983663,0.0003538366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002391593,0.0001202569,0.9875069,0.01001497,0.00004326877,0.0001547974,0.00000118337,0.0003617809,0.001557657],"genre_scores_gemma":[0.006373737,0.00005887064,0.9892604,0.00352294,0.00002851421,0.00005176533,0.00001408732,0.00001051972,0.0006792076],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9687762,"threshold_uncertainty_score":0.9989746,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4408634392","doi":"10.1016/j.media.2025.103547","title":"Medical SAM adapter: Adapting segment anything model for medical image segmentation","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":334,"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":"Ministry of Education - Singapore; Ministry of Education; National University of Singapore","keywords":"Artificial intelligence; Computer vision; Segmentation; Computer science; Adapter (computing); Image segmentation; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01469714968837245,"gpt":0.3352634477158427,"spread":0.3205662980274702,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006977229,0.0004892831,0.001007738,0.001095405,0.0004427165,0.0004691545,0.003031694,0.0005943016,0.002889996],"category_scores_gemma":[0.009115491,0.000434802,0.0007162925,0.002755194,0.0005469976,0.001244618,0.001193131,0.0009451634,0.0000503491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003090378,"about_ca_system_score_gemma":0.0014681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001898297,"about_ca_topic_score_gemma":0.0001888555,"domain_scores_codex":[0.9892675,0.0004819465,0.001823362,0.001371003,0.006091568,0.0009646146],"domain_scores_gemma":[0.9950477,0.001398364,0.0003709428,0.001112987,0.0005340101,0.001535964],"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":[0.00005525917,0.0009018467,0.0004388085,0.0003359713,0.00231532,0.0005977997,0.001591345,0.0001742413,0.004025926,0.004752232,0.05010478,0.9347064],"study_design_scores_gemma":[0.001361277,0.00006092291,0.00006310279,0.0002488913,0.000552842,0.00001515203,0.0001818333,0.9857798,0.009410754,0.001615529,0.0003181327,0.0003917181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005427894,0.0002195633,0.9738762,0.02249456,0.0002731392,0.0005921128,0.00001431683,0.0007512849,0.001236049],"genre_scores_gemma":[0.03355499,0.0003991386,0.9393429,0.02409448,0.0003023284,0.000542451,0.0002292041,0.00004571771,0.001488801],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9856056,"threshold_uncertainty_score":0.9998104,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2513067461","doi":"10.1016/j.media.2016.08.007","title":"Brain shift in neuronavigation of brain tumors: A review","year":2016,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Glioma Diagnosis and Treatment","field":"Medicine","cited_by":318,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; McGill Genome Centre","funders":"Canadian Institutes of Health Research; Canadian HIV Trials Network, Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Neuronavigation; Paradigm shift; Computer science; Field (mathematics); Compensation (psychology); Brain activity and meditation; Medical physics; Artificial intelligence; Neuroscience; Medicine; Electroencephalography; Psychology; Magnetic resonance imaging; Radiology","retraction":null,"screen_n_in":null,"score":{"opus":0.02490259976076307,"gpt":0.3796284851693184,"spread":0.3547258854085553,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001387107,0.0004247808,0.004749847,0.0008959706,0.00001900907,0.00001131411,0.0002699299,0.0002693862,0.003880263],"category_scores_gemma":[0.004254292,0.000248473,0.00209377,0.002887073,0.0001664468,0.00006323827,0.00009929875,0.00039714,0.0002090853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001477859,"about_ca_system_score_gemma":0.000433296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001411896,"about_ca_topic_score_gemma":0.00007033631,"domain_scores_codex":[0.9957099,0.0005752291,0.001657548,0.000617072,0.001112029,0.0003282321],"domain_scores_gemma":[0.9969167,0.001210829,0.0006121341,0.0007899227,0.00008901909,0.0003813779],"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":[0.000003400337,0.0004536336,0.000251488,0.0338773,0.001759052,0.001586288,0.00001287506,1.226964e-8,7.920488e-7,0.00002556221,0.007156456,0.9548731],"study_design_scores_gemma":[0.0008092279,0.0001104683,0.0005868922,0.2432696,0.01861944,0.0000644972,0.000002294204,0.0000132622,0.000002545172,0.00002971511,0.7362518,0.0002402438],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002517352,0.9827179,0.0001404553,0.01591068,0.00002489316,0.0008246995,0.00005491331,0.00002259779,0.0002786717],"genre_scores_gemma":[0.00007858621,0.9964405,0.00008296251,0.002207137,0.0001067828,0.0002498926,0.0006794666,0.00003944516,0.0001151755],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9546329,"threshold_uncertainty_score":0.9999967,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2799738340","doi":"10.1016/j.media.2019.02.009","title":"Constrained-CNN losses for weakly supervised segmentation","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":282,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; Kahnawake Education Center","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leverage (statistics); Computer science; Segmentation; Iterated function; Mathematical optimization; Differentiable function; Artificial intelligence; Dual (grammatical number); Pixel; Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0113012703349794,"gpt":0.2936607132017627,"spread":0.2823594428667833,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000242667,0.0001127253,0.0002452605,0.0001536091,0.00008571257,0.00009525158,0.0006477513,0.00005592716,0.0005673956],"category_scores_gemma":[0.0001407747,0.00009848409,0.000203818,0.001425089,0.00008720926,0.0004222263,0.0001055419,0.00009644982,0.0002114994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000253768,"about_ca_system_score_gemma":0.00005594831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009539893,"about_ca_topic_score_gemma":0.000009577391,"domain_scores_codex":[0.9985665,0.00004173299,0.0002776969,0.000422414,0.0004495489,0.0002420844],"domain_scores_gemma":[0.9986756,0.0004115815,0.00008585292,0.0005238134,0.0001273703,0.0001757495],"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":[0.00004624775,0.0007138632,0.01954319,0.0001664109,0.002471037,0.00006733307,0.000680828,0.004452173,0.09364945,0.08169131,0.01148876,0.7850294],"study_design_scores_gemma":[0.0006435411,0.00005836511,0.001445551,0.00001000657,0.0002882908,0.000004990115,0.00005922544,0.9847953,0.005670786,0.002770979,0.004011978,0.0002409382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01911923,0.00004006743,0.9748856,0.004698119,0.00005805049,0.0002958975,0.000007144426,0.0001223386,0.0007735765],"genre_scores_gemma":[0.7087473,0.00005347203,0.2879939,0.002174629,0.0001183982,0.0001461319,0.0001154566,0.00001200701,0.0006386329],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9803432,"threshold_uncertainty_score":0.6212584,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392883923","doi":"10.1016/j.media.2024.103143","title":"CellViT: Vision Transformers for precise cell segmentation and classification","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":282,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Princess Margaret Cancer Centre","funders":"Universität Duisburg-Essen","keywords":"Computer science; Segmentation; Artificial intelligence; Convolutional neural network; Deep learning; Encoder; Pattern recognition (psychology); Cluster analysis; Computer vision; Scale-space segmentation; Image segmentation","retraction":null,"screen_n_in":null,"score":{"opus":0.01034864457144071,"gpt":0.308377914807403,"spread":0.2980292702359623,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005557722,0.00009068858,0.0001361004,0.000296212,0.00008800035,0.0002450128,0.0001779234,0.00008123225,0.0001016845],"category_scores_gemma":[0.00003470469,0.0000777243,0.0001397624,0.0011179,0.00005765159,0.0006418491,0.00002776021,0.000106845,0.00002382061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007769169,"about_ca_system_score_gemma":0.00005838477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001838192,"about_ca_topic_score_gemma":0.00002923841,"domain_scores_codex":[0.9987049,0.00004755077,0.0002258985,0.0004259445,0.000450094,0.0001456155],"domain_scores_gemma":[0.9994453,0.0001791863,0.00003650237,0.0001526836,0.00005363423,0.0001326861],"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":[0.000008897473,0.00003670826,0.00005263535,0.0001242688,0.000145619,0.000006190657,0.0006111917,0.00001885171,0.02617229,0.0001654685,0.00240853,0.9702494],"study_design_scores_gemma":[0.0001902091,0.00007324032,0.0004622704,0.00001693749,0.0003579828,0.000001904628,0.00005877757,0.9754264,0.0189446,0.0004472632,0.00392042,0.0001000429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004219302,0.0004404943,0.9912869,0.002905935,0.0001965465,0.0001723928,0.000004622819,0.000130252,0.0006434921],"genre_scores_gemma":[0.9611568,0.0005086013,0.03743423,0.000178844,0.0001057907,0.0001012845,0.00003451216,0.00001134404,0.0004686013],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9754075,"threshold_uncertainty_score":0.3169505,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2014940628","doi":"10.1016/j.media.2011.04.003","title":"New methods for MRI denoising based on sparseness and self-similarity","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":280,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research","keywords":"Thresholding; Artificial intelligence; Noise reduction; Pattern recognition (psychology); Similarity (geometry); Image denoising; Computer science; Exploit; Filter (signal processing); Discrete cosine transform; USable; Invariant (physics); Mathematics; Computer vision; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.03962570295564427,"gpt":0.3544762924274026,"spread":0.3148505894717584,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003421597,0.0002274568,0.0005427403,0.0004630818,0.0002107291,0.0002203358,0.00083615,0.0001618194,0.0002483514],"category_scores_gemma":[0.0008044072,0.0001890613,0.0003327841,0.001458428,0.00008509503,0.0003526881,0.000185129,0.0002364093,0.000008800708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003251364,"about_ca_system_score_gemma":0.0002023439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000238446,"about_ca_topic_score_gemma":0.00001826297,"domain_scores_codex":[0.9972583,0.0007305224,0.0003742469,0.0006870329,0.0005364881,0.0004134211],"domain_scores_gemma":[0.9973703,0.001104353,0.0001112712,0.0007428352,0.0001515446,0.0005196861],"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":[0.0000930171,0.0004420897,0.00123895,0.00006887822,0.0009398671,0.0002349176,0.001165514,0.00005577374,0.001642915,0.002446533,0.002052934,0.9896186],"study_design_scores_gemma":[0.001167218,0.0001585215,0.003470141,0.0000274835,0.001066929,0.000009859083,0.00001427845,0.9610282,0.02149818,0.006987162,0.004196288,0.0003757882],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003352003,0.0001804743,0.9966855,0.001105064,0.000141786,0.0001268926,0.000001099414,0.0001703172,0.001253642],"genre_scores_gemma":[0.006853841,0.00002577595,0.9904429,0.002389988,0.0001022614,0.00001089561,0.000004286605,0.00001441868,0.0001556644],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9892429,"threshold_uncertainty_score":0.7709696,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2003863798","doi":"10.1016/j.media.2010.05.010","title":"Non-local MRI upsampling","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":274,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Upsampling; Interpolation (computer graphics); Artificial intelligence; Computer science; Computer vision; Bicubic interpolation; Image scaling; Constraint (computer-aided design); Coherence (philosophical gambling strategy); Nearest-neighbor interpolation; Image (mathematics); Mathematics; Algorithm; Multivariate interpolation; Image processing; Bilinear interpolation; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.005285407020192026,"gpt":0.2967542073361372,"spread":0.2914688003159452,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007620334,0.0001673858,0.0003322032,0.0003626524,0.0001571323,0.0002182035,0.001766351,0.0001438532,0.0003960351],"category_scores_gemma":[0.0005419854,0.0001427015,0.0002183545,0.002070073,0.0003268209,0.0007682737,0.0005455777,0.0006722446,0.0001046932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002059975,"about_ca_system_score_gemma":0.0001129351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005406667,"about_ca_topic_score_gemma":0.00006119747,"domain_scores_codex":[0.9978719,0.0000258716,0.0003289783,0.0005590096,0.0008307881,0.0003834885],"domain_scores_gemma":[0.9983027,0.0001179865,0.0001030345,0.0009366393,0.0001880155,0.0003516369],"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":[0.00001357931,0.0007092946,0.006775966,0.00008994345,0.001294996,0.001354686,0.001207883,0.000302772,0.06470112,0.01003963,0.01080802,0.9027021],"study_design_scores_gemma":[0.0001129896,0.00001446484,0.0003833112,0.000009897393,0.0001178712,0.00001769123,0.00001082007,0.9834045,0.007207192,0.006160097,0.002350816,0.0002103496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008661858,0.00003161174,0.993889,0.003061154,0.000121476,0.00004609759,6.755544e-7,0.0005498181,0.001434002],"genre_scores_gemma":[0.185953,0.00001434788,0.8128505,0.0009180659,0.0001039811,0.00001543247,0.000004823892,0.00001030197,0.000129477],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9831017,"threshold_uncertainty_score":0.58192,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2057441779","doi":"10.1016/j.media.2014.10.004","title":"Right ventricle segmentation from cardiac MRI: A collation study","year":2014,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":242,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University; CARE Canada","funders":"Medical Research Council; National Science Council; Ministerio de Ciencia e Innovación; British Heart Foundation; Engineering and Physical Sciences Research Council; Comunidad de Madrid","keywords":"Hausdorff distance; Segmentation; Computer science; Artificial intelligence; Tracing; Metric (unit); Pattern recognition (psychology); Computer vision; Ventricle; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.005184458891641832,"gpt":0.3344543014174768,"spread":0.329269842525835,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002620025,0.0001251551,0.0004849849,0.0001814805,0.00008210539,0.00003628726,0.00005249178,0.00005611253,0.002769085],"category_scores_gemma":[0.0002090953,0.00009836753,0.001044542,0.0008558116,0.00003507929,0.0000733126,0.00003259191,0.00007823001,0.0002004895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008664466,"about_ca_system_score_gemma":0.00004370267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008333034,"about_ca_topic_score_gemma":0.0000313906,"domain_scores_codex":[0.9981651,0.0001817782,0.0002624448,0.0003222448,0.0009055677,0.0001628893],"domain_scores_gemma":[0.9990149,0.0001172075,0.00007158797,0.0003398297,0.0001073178,0.0003491488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006109179,0.0007691584,0.9791433,0.000004002635,0.009038249,0.00005045549,0.0001428275,0.000006900107,0.0001557773,0.000004110281,0.0007865799,0.009837528],"study_design_scores_gemma":[0.002184983,0.0001394683,0.9649914,0.000009253728,0.02562369,3.33456e-7,0.0004004903,0.005589893,0.0003170173,0.00004776209,0.0005978284,0.00009786756],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921397,0.0002635947,0.00543326,0.0003429684,0.00009087827,0.000366754,0.00003542805,0.00005225822,0.001275165],"genre_scores_gemma":[0.9981598,0.00003761934,0.0003266437,0.0002120289,0.0002640944,0.00004471332,0.0007875902,0.00001183888,0.0001557032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01658544,"threshold_uncertainty_score":0.9981425,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2045105614","doi":"10.1016/j.media.2010.03.001","title":"Robust Rician noise estimation for MR images","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":237,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Robustness (evolution); Computer science; Artificial intelligence; Estimator; Gaussian noise; Noise (video); Rician fading; Ghosting; Wavelet; Noise reduction; Pattern recognition (psychology); Computer vision; Mathematics; Algorithm; Statistics; Image (mathematics); Fading","retraction":null,"screen_n_in":null,"score":{"opus":0.01805896654669985,"gpt":0.3059172455766335,"spread":0.2878582790299337,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002072856,0.0001748392,0.0003830026,0.00043062,0.0002267794,0.0003854502,0.001104288,0.0001482791,0.0003864529],"category_scores_gemma":[0.002530908,0.0001454888,0.0003927687,0.001638595,0.0001542207,0.0006904716,0.0001675742,0.0003463309,0.00008279414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001706614,"about_ca_system_score_gemma":0.0001044667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009344031,"about_ca_topic_score_gemma":0.00005165039,"domain_scores_codex":[0.9978508,0.0001380278,0.0003832648,0.0005110342,0.0007355973,0.0003812794],"domain_scores_gemma":[0.9980203,0.0005280182,0.0001219555,0.0007444758,0.0002725667,0.0003126963],"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":[0.00002989884,0.0003821307,0.0007484479,0.00006354103,0.0008508639,0.00025676,0.0004888788,0.001036898,0.06397067,0.003996859,0.01854786,0.9096272],"study_design_scores_gemma":[0.0005235444,0.00003463066,0.001751565,0.000007498978,0.0004603852,0.00001203295,0.000008253012,0.9776875,0.01476839,0.002903824,0.001609751,0.0002326588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003044785,0.00004264957,0.990844,0.004175003,0.0003017181,0.0001182899,0.000005194689,0.0001619258,0.001306477],"genre_scores_gemma":[0.09665399,0.000007659811,0.900902,0.001264423,0.0002407554,0.00003284183,0.00003046385,0.00001328689,0.0008546305],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9766506,"threshold_uncertainty_score":0.5932863,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2013160622","doi":"10.1016/j.media.2013.03.009","title":"Tractometer: Towards validation of tractography pipelines","year":2013,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":236,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Tractography; Computer science; Artificial intelligence; Seeding; Pattern recognition (psychology); Computer vision; Mathematics; Diffusion MRI; Engineering; Magnetic resonance imaging","retraction":null,"screen_n_in":null,"score":{"opus":0.04430028071975608,"gpt":0.3782283427137563,"spread":0.3339280619940003,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001769449,0.0001044217,0.0003765496,0.0004567918,0.00002875122,0.00001436521,0.0001307727,0.00007100028,0.002727919],"category_scores_gemma":[0.0003483857,0.00007989846,0.0003770902,0.001697799,0.0001498732,0.0001400172,0.00002742389,0.0001748827,0.00002922427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009040467,"about_ca_system_score_gemma":0.00003133482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000179759,"about_ca_topic_score_gemma":0.000001662071,"domain_scores_codex":[0.998696,0.0000269169,0.0003943937,0.0002257962,0.0005112542,0.0001456583],"domain_scores_gemma":[0.9989781,0.00006824826,0.0001207484,0.0003799079,0.0002499278,0.0002030744],"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":[0.00004945715,0.002442563,0.1215474,0.0002402934,0.002186107,0.00007319258,0.0002694623,0.00001439045,0.2814159,0.0002665104,0.01426026,0.5772345],"study_design_scores_gemma":[0.001601638,0.000337491,0.4792034,0.0001185654,0.00623776,0.00006589269,0.0001780704,0.01964038,0.4743885,0.003619843,0.01405516,0.0005532997],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5676335,0.0000827031,0.4217497,0.008003238,0.00001484765,0.0002840244,0.000009957248,0.0001741704,0.002047843],"genre_scores_gemma":[0.9648417,0.0001549589,0.03403865,0.0005815155,0.00005709708,0.00006138909,0.00008917035,0.00001225019,0.0001633006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5766812,"threshold_uncertainty_score":0.9981837,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2589644515","doi":"10.1016/j.media.2017.11.005","title":"Learning normalized inputs for iterative estimation in medical image segmentation","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":229,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Computer science; Pipeline (software); Segmentation; Convolutional neural network; Artificial intelligence; Benchmark (surveying); Deep learning; Residual; Image segmentation; Pattern recognition (psychology); Computer vision; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.00796106501721744,"gpt":0.3132501830028003,"spread":0.3052891179855828,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001615724,0.0002260321,0.0006245942,0.0004940872,0.000295987,0.0002938892,0.0004772674,0.0001970773,0.002311855],"category_scores_gemma":[0.006033029,0.0002008405,0.0003308536,0.0005573875,0.0002503605,0.0006131686,0.00007988804,0.0005318856,0.00006422897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000854138,"about_ca_system_score_gemma":0.00006053718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00017869,"about_ca_topic_score_gemma":0.0001935958,"domain_scores_codex":[0.9973022,0.0001330942,0.0006428875,0.0003477915,0.001166388,0.0004076904],"domain_scores_gemma":[0.9986051,0.0003040189,0.0001460732,0.0003747853,0.0001200135,0.0004499815],"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":[0.00008047334,0.0005998332,0.0773045,0.0008994309,0.007785271,0.001427946,0.005730023,0.0317929,0.01404973,0.000128184,0.008825431,0.8513763],"study_design_scores_gemma":[0.001176851,0.00001669173,0.002810479,0.00009100547,0.0006621415,0.000003293077,0.00009647256,0.9925235,0.00199444,0.00009220414,0.0003161137,0.0002168405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09953765,0.00007797196,0.8950861,0.003205884,0.0001173933,0.0001444503,0.000008789583,0.0001766932,0.001645078],"genre_scores_gemma":[0.9811086,0.0001766675,0.01735086,0.0003516992,0.0001653086,0.0001047801,0.0003814539,0.00003206052,0.0003285824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9607306,"threshold_uncertainty_score":0.9986002,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2158649156","doi":"10.1016/j.media.2004.06.009","title":"Tuning and comparing spatial normalization methods","year":2004,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":221,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Normalization (sociology); Spatial normalization; Computer science; Artificial intelligence; Image registration; Algorithm; Range (aeronautics); Process (computing); Computer vision; Pattern recognition (psychology); Image (mathematics); Voxel","retraction":null,"screen_n_in":null,"score":{"opus":0.01701380451331797,"gpt":0.3596544167055799,"spread":0.3426406121922619,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001320811,0.0001184556,0.0003096598,0.0003577239,0.000118957,0.0001900592,0.0005123242,0.00007624339,0.0002561587],"category_scores_gemma":[0.0008187642,0.0001048016,0.00009455473,0.001344722,0.0001675395,0.0005827514,0.000333232,0.0001885062,0.00001376415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004477879,"about_ca_system_score_gemma":0.0000643148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006681653,"about_ca_topic_score_gemma":0.00009688716,"domain_scores_codex":[0.9981279,0.000212664,0.0003687598,0.0003603374,0.0007098553,0.000220485],"domain_scores_gemma":[0.9990189,0.0001065707,0.0001044562,0.0003351869,0.00009442331,0.000340482],"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":[0.000004864957,0.00017553,0.01041273,0.0000478784,0.000632988,0.0002189182,0.001950828,0.0001926689,0.00890336,0.00369737,0.0002869747,0.9734759],"study_design_scores_gemma":[0.0007235616,0.00004730203,0.005872691,0.00004337212,0.0003585302,0.00002345408,0.00005639195,0.9461278,0.04386127,0.002455618,0.0001453297,0.0002847092],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001901331,0.00007964429,0.995826,0.001164392,0.00004985179,0.00006240285,2.428688e-7,0.0002613497,0.0006547512],"genre_scores_gemma":[0.2008804,0.00005522185,0.7979378,0.001023153,0.00004617471,0.000009834174,0.00001403267,0.000005682803,0.00002778669],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9731912,"threshold_uncertainty_score":0.4273685,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2888443510","doi":"10.1016/j.media.2018.08.005","title":"Spine-GAN: Semantic segmentation of multiple spinal structures","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":215,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Segmentation; Computer science; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Concatenation (mathematics); Computer vision; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.007484894842775308,"gpt":0.27293141559568,"spread":0.2654465207529047,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000328921,0.0001621659,0.0004628031,0.0003962535,0.00005688596,0.00002851942,0.0002473404,0.00009116081,0.003258341],"category_scores_gemma":[0.0005087292,0.0001327817,0.0002930595,0.001616835,0.0003340897,0.0001046053,0.00003033013,0.0001826339,0.00006453576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000239781,"about_ca_system_score_gemma":0.00002185099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001672494,"about_ca_topic_score_gemma":0.00009518809,"domain_scores_codex":[0.9981835,0.00005014722,0.0004683992,0.0002268142,0.000806484,0.0002646644],"domain_scores_gemma":[0.9991916,0.00004370778,0.00006617187,0.0003013426,0.0001264577,0.0002707005],"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":[0.00004812781,0.0005075666,0.1954446,0.001372695,0.02272337,0.0004415877,0.002062297,0.005463111,0.2865757,0.0002170637,0.02118663,0.4639573],"study_design_scores_gemma":[0.0005026392,0.00004990913,0.02051612,0.0000558246,0.002486893,0.00000555293,0.0001592042,0.9219974,0.05333039,0.0001496981,0.0004928932,0.0002534305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4736246,0.0003186875,0.5240651,0.0003466078,0.0001528701,0.00005241117,0.000007286718,0.0001708102,0.001261631],"genre_scores_gemma":[0.994487,0.00007160411,0.00482116,0.0001509127,0.0003045198,0.000004261472,0.00005192708,0.00001665048,0.00009197662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9165343,"threshold_uncertainty_score":0.9976528,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2810924486","doi":"10.1016/j.media.2018.07.001","title":"Synthesizing retinal and neuronal images with generative adversarial nets","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":214,"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":"Chinese Government Scholarship; China Scholarship Council; Government of Jiangxi Province","keywords":"Computer science; Artificial intelligence; Annotation; Set (abstract data type); Generative grammar; Image (mathematics); Pattern recognition (psychology); Computer vision","retraction":null,"screen_n_in":null,"score":{"opus":0.007527008097328543,"gpt":0.2789910835645772,"spread":0.2714640754672487,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005679916,0.0002451527,0.0006882161,0.0003998464,0.0002233838,0.00009788508,0.0001343435,0.00008900345,0.002047432],"category_scores_gemma":[0.0008481909,0.0001664785,0.0002582573,0.001208471,0.00114935,0.0001543218,0.00008352619,0.0003441422,0.0000532244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002898989,"about_ca_system_score_gemma":0.0001284481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002497398,"about_ca_topic_score_gemma":0.00005716708,"domain_scores_codex":[0.9975199,0.000164107,0.0003438257,0.0005911402,0.001004293,0.0003767596],"domain_scores_gemma":[0.9985039,0.000170848,0.0001188968,0.0003448608,0.0003280109,0.0005334881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003985856,0.001411516,0.6746113,0.0002909878,0.03037152,0.009852442,0.002875102,0.00003326619,0.09119135,0.0002408398,0.03184532,0.1532905],"study_design_scores_gemma":[0.01100939,0.004857241,0.4430304,0.00107756,0.09240039,0.0022178,0.002792263,0.327174,0.0916795,0.0002736388,0.02078563,0.002702103],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8664334,0.0003571291,0.09935425,0.02295301,0.00006807947,0.0001631577,0.00001308118,0.0001551728,0.01050271],"genre_scores_gemma":[0.9806896,0.00009931054,0.01485429,0.002246696,0.0008310125,0.000007784129,0.00004125201,0.00002557037,0.001204486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3271408,"threshold_uncertainty_score":0.9988648,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3138973186","doi":"10.1016/j.media.2021.102032","title":"Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":211,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; McMaster University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario; Ontario Research Foundation","keywords":"Representation (politics); Artificial intelligence; Computer science; Training (meteorology); Image (mathematics); Computer vision; Pattern recognition (psychology); Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.03861681853481116,"gpt":0.3334691023019692,"spread":0.294852283767158,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006737951,0.00009199422,0.0003505855,0.0002201322,0.00009916785,0.00005799968,0.0001987846,0.00007237677,0.00007112986],"category_scores_gemma":[0.006141423,0.00009499403,0.000141826,0.001029728,0.0001572256,0.0002837449,0.0001529406,0.0001019202,7.420542e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004713805,"about_ca_system_score_gemma":0.0001259051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009722673,"about_ca_topic_score_gemma":0.0002027892,"domain_scores_codex":[0.9984846,0.0002126831,0.0003584442,0.0004210125,0.0003334514,0.0001898193],"domain_scores_gemma":[0.9975164,0.001658096,0.0001675569,0.0003311851,0.0002232959,0.0001034684],"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":[0.00005335182,0.0001980125,0.03594834,0.0004981656,0.001057677,0.002005046,0.01006636,0.002474005,0.4242588,0.0007463442,0.0007269772,0.5219669],"study_design_scores_gemma":[0.0005281849,0.00005968983,0.01104992,0.0001176889,0.0008519896,0.0001264648,0.0003450871,0.9474766,0.03782684,0.001369786,0.00005244182,0.0001953307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1693916,0.0003385565,0.8293721,0.0006545772,0.00009920292,0.00005796645,0.000002717452,0.00002836917,0.00005490412],"genre_scores_gemma":[0.7488397,0.00005882743,0.2508065,0.0001351006,0.00008654246,0.00001719511,0.00001237707,0.000007974128,0.0000357307],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9450026,"threshold_uncertainty_score":0.7352301,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2793804994","doi":"10.1016/j.media.2018.02.002","title":"Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":203,"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":"Pacific Alzheimer Research Foundation; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; Eisai; U.S. Department of Defense; Eli Lilly and Company; Lundbeckfonden; Michael Smith Health Research BC; AbbVie; Fondation Brain Canada; DoD Alzheimer's Disease Neuroimaging Initiative; Natural Sciences and Engineering Research Council of Canada; Pfizer; BioClinica; Biogen; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; F. Hoffmann-La Roche; Roche; Merck; Alzheimer's Drug Discovery Foundation; Takeda Pharmaceutical Company; Fujirebio Europe; Alzheimer's Association; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics","keywords":"Dementia; Prodromal Stage; Positron emission tomography; Neuroimaging; Disease; Cognition; Psychology; Medicine; Cognitive impairment; Alzheimer's disease; Neuroscience; Pathology","retraction":null,"screen_n_in":null,"score":{"opus":0.02038795846898045,"gpt":0.3494687554062398,"spread":0.3290807969372594,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001321888,0.000213998,0.0009769719,0.0006731075,0.0001589206,0.00003270712,0.0003477021,0.00005952614,0.006761436],"category_scores_gemma":[0.001656701,0.0001351225,0.00164333,0.004158058,0.001140963,0.00009806809,0.0001204283,0.0001789491,0.00001087368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001716851,"about_ca_system_score_gemma":0.0001121789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000486078,"about_ca_topic_score_gemma":0.0002330274,"domain_scores_codex":[0.9966567,0.0002390014,0.00068951,0.0004317136,0.001475477,0.000507577],"domain_scores_gemma":[0.9963088,0.001298376,0.0002391731,0.0006597805,0.0009370694,0.0005568244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004907454,0.0006823079,0.9496022,0.00005393823,0.02323351,0.00007895025,0.00006211246,0.0001454105,0.0001575515,0.000004977003,0.00225158,0.02323665],"study_design_scores_gemma":[0.0008944765,0.000268195,0.6195759,0.00002278337,0.07272536,3.457722e-7,0.00005118949,0.3044589,0.001779495,0.00001069768,0.0001282224,0.00008443406],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8501457,0.001791349,0.1410913,0.005440086,0.00007285296,0.0009033657,0.0002196583,0.00003743933,0.000298302],"genre_scores_gemma":[0.9965566,0.0001150771,0.001677165,0.0007716707,0.0002400711,0.0002427008,0.0002484084,0.00001958189,0.0001286721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3300264,"threshold_uncertainty_score":0.9941465,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4200066891","doi":"10.1016/j.media.2021.102336","title":"Head and neck tumor segmentation in PET/CT: The HECKTOR challenge","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":203,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"BC Cancer Agency; Université de Sherbrooke","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Segmentation; Thresholding; Artificial intelligence; Computer science; Sørensen–Dice coefficient; Modality (human–computer interaction); Leverage (statistics); Positron emission tomography; Medicine; Nuclear medicine; Medical physics; Pattern recognition (psychology); Image segmentation; Computer vision; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.009524723942521691,"gpt":0.3205935885383795,"spread":0.3110688645958578,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008039452,0.000132149,0.0004403995,0.000177154,0.00008979,0.00005205758,0.0001043451,0.00001469418,0.001645344],"category_scores_gemma":[0.001772995,0.00008838154,0.0001673589,0.0009172283,0.0002092461,0.0000732952,0.00008859398,0.0006014485,0.0000219097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004746037,"about_ca_system_score_gemma":0.0001318005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003158035,"about_ca_topic_score_gemma":0.0002822911,"domain_scores_codex":[0.998179,0.000165132,0.0003570565,0.0003475502,0.0006884378,0.0002628715],"domain_scores_gemma":[0.9990677,0.0001981572,0.00006453217,0.0002894274,0.00007002192,0.0003101988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000153823,0.001914787,0.614062,0.0005927432,0.003778685,0.1041826,0.002801018,0.00006824236,0.005461352,0.0006970323,0.00682184,0.2594659],"study_design_scores_gemma":[0.008366926,0.0002466285,0.4561817,0.0005669863,0.004851155,0.004988658,0.004218274,0.4961246,0.001195922,0.0005351261,0.02209826,0.0006257548],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9315662,0.001685682,0.00222063,0.06232565,0.00007063656,0.0001114646,0.000001940773,0.00003198054,0.001985792],"genre_scores_gemma":[0.9880181,0.0009808907,0.003254615,0.006613506,0.0001948908,0.00002076658,0.00007440078,0.00001798357,0.0008248326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4960563,"threshold_uncertainty_score":0.9992673,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3041500444","doi":"10.1016/j.media.2020.101770","title":"Supervised learning with cyclegan for low-dose FDG PET image denoising","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":194,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Siemens (Canada); University Health Network","funders":"National University Cancer Institute, Singapore","keywords":"Artificial intelligence; Noise reduction; Image denoising; Computer science; Pattern recognition (psychology); Computer vision; Supervised learning; Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.01569687144382458,"gpt":0.3122459519873317,"spread":0.2965490805435071,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005159642,0.0002390979,0.000726627,0.0001830663,0.000195656,0.00008476505,0.0002833427,0.00009045738,0.002100034],"category_scores_gemma":[0.001922644,0.0001780772,0.0003977794,0.001483833,0.0003423656,0.0001393229,0.00009492382,0.0006290942,0.00006400408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003944257,"about_ca_system_score_gemma":0.0001744472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007507787,"about_ca_topic_score_gemma":0.000009929992,"domain_scores_codex":[0.9975172,0.00006991743,0.0004752213,0.0005868674,0.0009062563,0.0004445893],"domain_scores_gemma":[0.9978591,0.0002212395,0.0001137101,0.0003958447,0.0002604372,0.001149729],"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":[0.002480651,0.003467251,0.05389986,0.00358705,0.0128043,0.008671779,0.004384463,0.0001075537,0.6220152,0.001135281,0.1598252,0.1276214],"study_design_scores_gemma":[0.005659722,0.0008308559,0.002989137,0.0003861604,0.01012502,0.0001682773,0.0008695956,0.9282851,0.01845267,0.0001233587,0.03131316,0.0007969437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1966354,0.00005161424,0.7156268,0.08556194,0.00001090932,0.0005432566,0.00001305153,0.0005097593,0.001047271],"genre_scores_gemma":[0.8072988,0.00007347823,0.1822937,0.00886226,0.0004177228,0.0001516965,0.0004081685,0.00006459629,0.000429582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9281775,"threshold_uncertainty_score":0.9988122,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2160744602","doi":"10.1016/j.media.2004.06.026","title":"Flux driven automatic centerline extraction","year":2004,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":185,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada","keywords":"Skeletonization; Distance transform; Computer science; Boundary (topology); Artificial intelligence; Key (lock); Function (biology); Algorithm; Computer vision; Mathematics; Image (mathematics); Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.00872080128720197,"gpt":0.3128668158849142,"spread":0.3041460145977122,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00062333,0.0001590551,0.0003343565,0.0004580882,0.0001011062,0.0001734283,0.0009765759,0.0001105678,0.003155164],"category_scores_gemma":[0.0006876292,0.0001346878,0.0002658493,0.001941432,0.0001536932,0.000820782,0.0001985166,0.0002812977,0.0004118337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001245146,"about_ca_system_score_gemma":0.0001252243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001157906,"about_ca_topic_score_gemma":0.00004289062,"domain_scores_codex":[0.9972047,0.0001221735,0.0005223609,0.000448839,0.001389969,0.0003120133],"domain_scores_gemma":[0.9985215,0.0001057328,0.0001507416,0.0006389765,0.0001220994,0.0004609622],"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":[0.00000457058,0.00122162,0.001002508,0.00007566772,0.001447148,0.001478068,0.001145029,0.0001579171,0.01869013,0.00190217,0.01053283,0.9623423],"study_design_scores_gemma":[0.002303078,0.0002264338,0.008465043,0.0001619799,0.001192748,0.0001195851,0.0001411326,0.860925,0.1193229,0.004977555,0.001332556,0.0008319687],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003576692,0.00003270158,0.9895444,0.005133521,0.0001088094,0.0001071164,0.00000135913,0.0006942457,0.0008011676],"genre_scores_gemma":[0.2345612,0.00005263226,0.7611756,0.003468099,0.0001339917,0.00004108456,0.00007015403,0.00001378465,0.0004834617],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9615104,"threshold_uncertainty_score":0.9977561,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4380148842","doi":"10.1016/j.media.2023.102863","title":"A survey on deep learning for skin lesion segmentation","year":2023,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":178,"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":"Natural Sciences and Engineering Research Council of Canada; Google; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Canadian Institutes of Health Research; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação para a Ciência e a Tecnologia; Fundação de Amparo à Pesquisa do Estado de São Paulo; BC Cancer Foundation; National Science Foundation","keywords":"Artificial intelligence; Segmentation; Deep learning; Skin lesion; Computer vision; Computer science; Pattern recognition (psychology); Lesion; Medicine; Dermatology; Pathology","retraction":null,"screen_n_in":null,"score":{"opus":0.07588093704665991,"gpt":0.4023016891922042,"spread":0.3264207521455443,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001563356,0.0003147479,0.001851549,0.001170234,0.0001279872,0.00005264101,0.0001425088,0.00030073,0.001129532],"category_scores_gemma":[0.002429773,0.0002371592,0.001411306,0.002056305,0.0000458552,0.00002242571,0.0000744936,0.0005060798,0.0006686436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002130244,"about_ca_system_score_gemma":0.00009863966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002014128,"about_ca_topic_score_gemma":0.0005870186,"domain_scores_codex":[0.9970984,0.0004093984,0.0007183921,0.0005734476,0.0009110398,0.0002892619],"domain_scores_gemma":[0.9980539,0.0009040405,0.0002999104,0.0003364848,0.0001145212,0.0002911384],"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":[0.00003042838,0.00008773104,0.00001165171,0.004211416,0.003520106,0.0002195033,0.00001968029,0.000005768927,3.752188e-7,0.000001904079,0.002728313,0.9891631],"study_design_scores_gemma":[0.0005551257,0.0003228169,0.0001953267,0.002668045,0.02421193,0.00002253292,0.00005724268,0.006376795,0.000002161664,0.000002955745,0.9652937,0.0002913806],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000025863,0.8924015,0.1023564,0.0002819599,0.0004744297,0.002691712,0.00005207891,0.0004787392,0.001237301],"genre_scores_gemma":[0.0000208791,0.9823959,0.0002612926,0.0001914199,0.0002879436,0.0002344248,0.004267846,0.00007287734,0.01226745],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9888718,"threshold_uncertainty_score":0.9997836,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1996595747","doi":"10.1016/j.media.2010.12.003","title":"Evaluating intensity normalization on MRIs of human brain with multiple sclerosis","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":178,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University Health Centre; McGill University; NeuroRx Research (Canada)","funders":"","keywords":"Artificial intelligence; Computer science; Segmentation; Normalization (sociology); Pattern recognition (psychology); Bayesian probability; Voxel; Computer vision","retraction":null,"screen_n_in":null,"score":{"opus":0.04306000652787775,"gpt":0.3516884117110728,"spread":0.3086284051831951,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001894864,0.000149932,0.0003830196,0.0004347512,0.0001375776,0.00008445158,0.0007992762,0.0001102126,0.0008655478],"category_scores_gemma":[0.003112728,0.0001144602,0.0001490312,0.001830096,0.0003228651,0.0003999895,0.0002030693,0.0003880706,0.00001700478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002337161,"about_ca_system_score_gemma":0.00006992091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003833279,"about_ca_topic_score_gemma":0.0003589897,"domain_scores_codex":[0.9969947,0.0001788102,0.0004890453,0.0004364354,0.001673233,0.0002277409],"domain_scores_gemma":[0.9978762,0.0003687122,0.0002683744,0.0007281877,0.0004816055,0.0002769438],"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":[0.00003639152,0.0006646487,0.03552946,0.00005424882,0.0007016203,0.00005905701,0.001321242,0.00005495888,0.7398499,0.0006838215,0.003769066,0.2172756],"study_design_scores_gemma":[0.0009948574,0.0005307461,0.0501647,0.00009743339,0.0003629905,0.000005614887,0.0000702946,0.4390403,0.508195,0.000209353,0.00001998052,0.0003087119],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.216114,0.000002503915,0.782203,0.001159808,0.0000363387,0.0001057286,0.000001877088,0.0001426227,0.0002341294],"genre_scores_gemma":[0.7854133,0.000003061297,0.2128074,0.001616416,0.0000413073,0.00001743873,0.00003280396,0.000008598955,0.00005960296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5693956,"threshold_uncertainty_score":0.9477141,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2892938835","doi":"10.1016/j.media.2018.09.005","title":"Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":177,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"St. Paul's Hospital; Richmond Hospital; Vancouver General Hospital; BC Cancer Agency; University of British Columbia","funders":"Canadian Institutes of Health Research; Prostate Cancer Canada","keywords":"Grading (engineering); Histopathology; Artificial intelligence; Prostate cancer; Computer science; Computer vision; Prostate; Pattern recognition (psychology); Medicine; Medical physics; Cancer; Pathology; Internal medicine; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.01028252171814546,"gpt":0.2790481769492846,"spread":0.2687656552311391,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005227965,0.0001346935,0.0004671127,0.000456477,0.00007851027,0.00005049811,0.0005539363,0.00008925643,0.0007168854],"category_scores_gemma":[0.0008278433,0.0001217153,0.0001454262,0.001426303,0.0003213805,0.0004046157,0.0002107345,0.0002044891,0.00001538906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000183668,"about_ca_system_score_gemma":0.0001042029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003235901,"about_ca_topic_score_gemma":0.00132421,"domain_scores_codex":[0.9979355,0.000253992,0.0004916114,0.0004803007,0.0005461574,0.000292386],"domain_scores_gemma":[0.9988202,0.0003145265,0.0002242229,0.0004054414,0.0001109955,0.0001245905],"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":[0.0000400562,0.0001956909,0.2608996,0.00005305672,0.0004406582,0.0003737858,0.01418789,0.0003563311,0.03356126,0.00002014568,0.0008556707,0.6890158],"study_design_scores_gemma":[0.0006736153,0.00006506781,0.03133224,0.00007365064,0.0001271203,0.000003431756,0.0001491939,0.9492764,0.01747278,0.0003829992,0.0002700549,0.00017342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5740796,0.0002409358,0.4244905,0.0006080413,0.000196178,0.00007809191,0.000003116174,0.0001421304,0.0001613905],"genre_scores_gemma":[0.987359,0.00007825971,0.01213897,0.0001474942,0.00009886085,0.00005144859,0.000005491767,0.00001011441,0.0001103112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9489201,"threshold_uncertainty_score":0.7849392,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2429271550","doi":"10.1016/j.media.2016.06.011","title":"Open-source platforms for navigated image-guided interventions","year":2016,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":176,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Key (lock); Field (mathematics); Navigation system; Software; Data science; Software engineering; Human–computer interaction; Artificial intelligence; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.1471835370373235,"gpt":0.4849784130553579,"spread":0.3377948760180344,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001687983,0.000483927,0.003711509,0.0006811239,0.0001696592,0.000176704,0.0007786658,0.0005484237,0.02736099],"category_scores_gemma":[0.003756048,0.000278129,0.005007914,0.001959426,0.0002634698,0.0002150495,0.0004043627,0.0005780528,0.0007236926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001507405,"about_ca_system_score_gemma":0.0003548929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004868264,"about_ca_topic_score_gemma":0.00001764596,"domain_scores_codex":[0.9959844,0.0001141211,0.001765077,0.0007736631,0.0008505273,0.0005122179],"domain_scores_gemma":[0.9963191,0.001172108,0.0005653462,0.0007168953,0.0003524635,0.0008741241],"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":[0.00002507033,0.000203889,0.00000993634,0.005347515,0.006405718,0.0001365834,0.00001605232,1.833712e-7,2.382652e-7,0.00006665822,0.002600182,0.9851879],"study_design_scores_gemma":[0.003029424,0.00007127062,0.000007393644,0.02400704,0.02298513,0.00004812556,0.00001944396,0.001040009,0.000001091872,0.00007267267,0.9483832,0.0003352146],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000007334378,0.8491046,0.1405645,0.0006365616,0.0001464216,0.001653734,0.0001544672,0.0002115734,0.007520863],"genre_scores_gemma":[0.00005930539,0.9800517,0.002652657,0.0005254524,0.0004333416,0.0003949585,0.003376361,0.0001103345,0.0123959],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9848527,"threshold_uncertainty_score":0.9999671,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3023773746","doi":"10.1016/j.media.2020.101714","title":"The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":171,"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 Institutes of Health; IXICO; H. Lundbeck A/S; Servier; Karolinska Institutet; Alzheimerfonden; Swedish Brain Power; Hjärnfonden; Vetenskapsrådet; Eisai; Stiftelsen Olle Engkvist Byggmästare; Genentech; Stiftelsen för Strategisk Forskning; Center for Innovative Medicine; Northern California Institute for Research and Education; Stockholms Läns Landsting; DoD Alzheimer's Disease Neuroimaging Initiative; Pfizer; Biogen; BioClinica; Nvidia; University of Southern California; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Alzheimer's Association; Åke Wiberg Stiftelse","keywords":"Artificial intelligence; Computer science; Reliability (semiconductor); Deep learning; Convolutional neural network; Protocol (science); Machine learning; Neuroradiologist; Neuroimaging; Medical physics; Pattern recognition (psychology); Magnetic resonance imaging; Medicine; Pathology; Radiology","retraction":null,"screen_n_in":null,"score":{"opus":0.07275462749978208,"gpt":0.4260945749124501,"spread":0.353339947412668,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005142118,0.0001321115,0.0009649502,0.00006642455,0.00004865484,0.00001079993,0.0005306237,0.0001294066,0.0002044135],"category_scores_gemma":[0.01951907,0.00008986767,0.0003165038,0.001243503,0.0004369591,0.00009387089,0.000412549,0.0007684733,0.000007988167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006477437,"about_ca_system_score_gemma":0.0001997015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003989371,"about_ca_topic_score_gemma":0.0008256701,"domain_scores_codex":[0.995982,0.0005631977,0.001459406,0.0005866549,0.001181504,0.0002272278],"domain_scores_gemma":[0.9966168,0.001437261,0.000299814,0.001043761,0.0002789497,0.0003234314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002620619,0.001335287,0.9785652,0.00007075765,0.0009654597,0.00005494041,0.000828448,0.002303418,0.00002038166,0.000002101244,0.001334495,0.01425747],"study_design_scores_gemma":[0.00130025,0.0002653491,0.1328674,0.0000266522,0.002971399,2.498609e-7,0.0008582703,0.8611294,0.00003562405,0.000009272422,0.0004690067,0.00006711841],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7885395,0.0002940293,0.1911619,0.01899629,0.00007581964,0.0006958004,0.00006160379,0.0000415279,0.0001335305],"genre_scores_gemma":[0.9968489,0.0008575664,0.001691557,0.0002425738,0.00009778951,0.00002959737,0.0002083627,0.00001043405,0.00001326217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.858826,"threshold_uncertainty_score":0.9887399,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1971405799","doi":"10.1016/j.media.2010.07.001","title":"Multiple q-shell diffusion propagator imaging","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":169,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"Association France Parkinson","keywords":"Diffusion MRI; Propagator; Computer science; Diffusion; Laplace transform; Fourier transform; SIGNAL (programming language); Diffusion equation; Algorithm; Artificial intelligence; Physics; Computer vision; Mathematical analysis; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01894291456037772,"gpt":0.3416434039444404,"spread":0.3227004893840627,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000235644,0.0001438435,0.0003275968,0.0002425825,0.0001203227,0.00002679326,0.0001932461,0.00006994534,0.001720705],"category_scores_gemma":[0.0009867597,0.0001108234,0.0002691768,0.0009236287,0.0002362314,0.00009042416,0.0001148821,0.0006041123,0.00009110149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001633017,"about_ca_system_score_gemma":0.00005601537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006760735,"about_ca_topic_score_gemma":0.00003232197,"domain_scores_codex":[0.9984994,0.00002026268,0.0002887067,0.000399588,0.0005340825,0.0002579843],"domain_scores_gemma":[0.9986339,0.00009602837,0.00007448857,0.000643662,0.0001278223,0.0004241116],"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":[0.00002989981,0.0006289234,0.3552921,0.00003588832,0.0001963858,0.0003828528,0.00004913628,6.728276e-7,0.5675464,0.0002291886,0.005626551,0.06998204],"study_design_scores_gemma":[0.003295094,0.0001050564,0.2246842,0.000106515,0.005301327,0.0004360357,0.0001127145,0.443599,0.1010397,0.001559081,0.2188231,0.0009381081],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5510086,0.000061916,0.4227734,0.02026194,0.00008896166,0.0004614682,0.00001365701,0.0007629416,0.004567103],"genre_scores_gemma":[0.9516765,0.00004442142,0.04513265,0.002092417,0.0001987924,0.00005624264,0.00007539002,0.00002609,0.0006974613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4665066,"threshold_uncertainty_score":0.9991919,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4309630525","doi":"10.1016/j.media.2022.102684","title":"Guidelines and evaluation of clinical explainable AI in medical image analysis","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":168,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"BC Cancer Foundation","keywords":"Guideline; Computer science; Data mining; Artificial intelligence; Medical physics; Medicine; Pathology","retraction":null,"screen_n_in":null,"score":{"opus":0.106462754669057,"gpt":0.4673112791791384,"spread":0.3608485245100814,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03186496,0.0002342665,0.001226778,0.001799347,0.000241636,0.0001255317,0.001850192,0.0001830551,0.007003912],"category_scores_gemma":[0.01511698,0.000217691,0.0006793704,0.01144297,0.0004437199,0.0007372114,0.00146413,0.000756394,0.00002528405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001319545,"about_ca_system_score_gemma":0.0008804736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00260452,"about_ca_topic_score_gemma":0.002236819,"domain_scores_codex":[0.9876338,0.002349663,0.002491142,0.001045174,0.005939686,0.0005405658],"domain_scores_gemma":[0.9955252,0.001023817,0.0004635105,0.001155003,0.001260175,0.0005723198],"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":[0.0001374334,0.003131842,0.1890351,0.00009712,0.009828517,0.002493722,0.003499475,0.02091805,0.0006025819,0.007407364,0.01603569,0.7468131],"study_design_scores_gemma":[0.0004191904,0.00008315849,0.008704822,0.00001054392,0.002200094,0.000008409175,0.000725102,0.9841737,0.0003265788,0.002186808,0.000944874,0.0002166758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3209193,0.0007578935,0.6552469,0.02126199,0.0002184164,0.0002837507,0.000008958584,0.00008436309,0.001218361],"genre_scores_gemma":[0.9821557,0.0002305511,0.01441965,0.002694298,0.000126194,0.0001215047,0.00005066841,0.00001384424,0.0001876347],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9632557,"threshold_uncertainty_score":0.9968988,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2979808779","doi":"10.1016/j.media.2019.101587","title":"‘Squeeze &amp; excite’ guided few-shot segmentation of volumetric images","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":166,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst; Nvidia","keywords":"Artificial intelligence; Segmentation; Computer vision; Shot (pellet); Computer science; Image segmentation; Pattern recognition (psychology); Chemistry","retraction":null,"screen_n_in":null,"score":{"opus":0.02131291264568962,"gpt":0.3229343077216302,"spread":0.3016213950759406,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004442904,0.0001501142,0.000395899,0.0005887245,0.00006443942,0.00006270446,0.001057788,0.00007648968,0.001266647],"category_scores_gemma":[0.0003414723,0.0001340413,0.000262061,0.006355573,0.0001117646,0.0005311696,0.0002913912,0.0001739631,0.0004226684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000049341,"about_ca_system_score_gemma":0.00005150311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008091301,"about_ca_topic_score_gemma":0.0000312041,"domain_scores_codex":[0.9975795,0.0001116384,0.0005333631,0.0005181447,0.0009789874,0.0002783639],"domain_scores_gemma":[0.9979857,0.0003235975,0.0002574302,0.001002112,0.0002256839,0.0002055018],"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":[0.00004087897,0.001559826,0.1664033,0.0002511173,0.003665038,0.0001054066,0.001257339,0.01291062,0.2499251,0.007589828,0.08040439,0.4758871],"study_design_scores_gemma":[0.002530073,0.0001889161,0.09153708,0.00006829717,0.001698497,0.00003858698,0.000102839,0.8280067,0.05212358,0.006034432,0.01627488,0.001396133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05593978,0.0001338594,0.9407126,0.001395979,0.00007197394,0.0001795084,0.000004862165,0.0001027349,0.001458649],"genre_scores_gemma":[0.7573558,0.0001568392,0.2388248,0.0008209337,0.00009420799,0.00004516517,0.00008520881,0.00001582725,0.002601268],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8150961,"threshold_uncertainty_score":0.9996463,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2913510405","doi":"10.1016/j.media.2019.01.013","title":"Weakly supervised mitosis detection in breast histopathology images using concentric loss","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":165,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University Health Network","funders":"National Science Foundation of Sri Lanka; National Natural Science Foundation of China","keywords":"Histopathology; Concentric; Artificial intelligence; Pattern recognition (psychology); Mathematics; Computer vision; Computer science; Medicine; Pathology; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.006263077141884169,"gpt":0.2467378444246055,"spread":0.2404747672827213,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006981412,0.0001791619,0.0004551699,0.0006697836,0.00007696652,0.0000917824,0.0007243248,0.0001790135,0.0006458531],"category_scores_gemma":[0.000117854,0.0001737124,0.0002432212,0.003940582,0.0001471567,0.0006324168,0.000235553,0.0003456446,0.0001369069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004481769,"about_ca_system_score_gemma":0.0001193014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001133344,"about_ca_topic_score_gemma":0.0002044171,"domain_scores_codex":[0.9974316,0.0003107317,0.0004406107,0.000680641,0.0007200292,0.0004164216],"domain_scores_gemma":[0.9987733,0.0001317588,0.0001365415,0.0006457132,0.0001313888,0.0001812733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008690777,0.0004102154,0.4322473,0.000107142,0.0005081025,0.001147412,0.001004884,0.001196173,0.2169334,0.00008478644,0.0002629126,0.3460107],"study_design_scores_gemma":[0.0008313375,0.00005618789,0.09784705,0.00002769249,0.0003045065,0.0002464177,0.00005194376,0.8851362,0.01476085,0.0001434862,0.0002451879,0.0003491125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4338773,0.0001389914,0.5643834,0.0009130166,0.0003560812,0.00008726415,0.00000243992,0.00008305985,0.0001583662],"genre_scores_gemma":[0.9917947,0.00006102717,0.007560911,0.0003755827,0.0001053387,0.00001158296,0.000003550135,0.00001177808,0.00007550117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.88394,"threshold_uncertainty_score":0.7083786,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2757633676","doi":"10.1016/j.media.2017.09.005","title":"Full left ventricle quantification via deep multitask relationships learning","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":161,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Softmax function; Multi-task learning; Artificial intelligence; Deep learning; Computer science; Pattern recognition (psychology); Classifier (UML); Convolutional neural network; Ventricle; Machine learning; Task (project management); Cardiology; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.02350027649408587,"gpt":0.3230192351613346,"spread":0.2995189586672488,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001061248,0.0001221205,0.0003827517,0.0002629715,0.0006676643,0.0001367302,0.0001772787,0.0001255059,0.0009682412],"category_scores_gemma":[0.01905605,0.0001104126,0.0003791849,0.0002919083,0.0002262777,0.0001676559,0.00007400298,0.0005714475,0.0006157886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005183289,"about_ca_system_score_gemma":0.00006000157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002608131,"about_ca_topic_score_gemma":0.0001085866,"domain_scores_codex":[0.9982912,0.0001376703,0.0003246268,0.0003141997,0.0006889049,0.0002433922],"domain_scores_gemma":[0.9980194,0.0004453027,0.000190433,0.0007589257,0.0002265737,0.0003593536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004455298,0.0001881675,0.9746234,0.00002821621,0.0008271983,0.000284442,0.000244253,0.0001436988,0.001277613,0.00003586107,0.001231903,0.02107071],"study_design_scores_gemma":[0.0008844348,0.00004378064,0.8136407,0.00003945623,0.004158282,0.00004625551,0.0001680924,0.1774699,0.0006697802,0.00005722202,0.002659714,0.0001623404],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3632933,0.0008664184,0.6168538,0.01184078,0.0002872272,0.0002146733,0.000006383243,0.0002261051,0.006411282],"genre_scores_gemma":[0.9957969,0.00008255114,0.002696568,0.0001710654,0.0002412148,0.000006205356,0.0001846028,0.00001662311,0.0008043158],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6325035,"threshold_uncertainty_score":0.999945,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2917393555","doi":"10.1016/j.media.2019.02.011","title":"Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":160,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Fondation pour la Recherche sur Alzheimer; Genome British Columbia; Simon Fraser University; Michael Smith Health Research BC; Canadian Institutes of Health Research; Alzheimer Society; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Optical coherence tomography; Artificial intelligence; Computer science; Convolutional neural network; Segmentation; Retinal; Pattern recognition (psychology); Computer vision; Cut; Retina; Pixel; Image segmentation; Ophthalmology; Medicine; Optics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.007756989084423179,"gpt":0.2748887174620818,"spread":0.2671317283776586,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009642023,0.0002160341,0.0006000248,0.0006131512,0.0001255082,0.00008451418,0.0000891763,0.0001296647,0.0008414329],"category_scores_gemma":[0.0005528582,0.000191783,0.0003318312,0.002192515,0.0003004459,0.0001864798,0.00005094709,0.0005624504,0.00001854552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009180622,"about_ca_system_score_gemma":0.00006782568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003047552,"about_ca_topic_score_gemma":0.00005928088,"domain_scores_codex":[0.9973518,0.0003000829,0.0005156581,0.000529384,0.0008828038,0.000420236],"domain_scores_gemma":[0.9988341,0.0002947666,0.0001415257,0.0001989844,0.0002060814,0.0003245745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003066237,0.0001547691,0.9010406,0.00009854491,0.0006218084,0.0002557713,0.00007793517,0.02009266,0.06319029,0.000002973347,0.00001692424,0.01414106],"study_design_scores_gemma":[0.001162061,0.0001225484,0.1859899,0.00009548943,0.001639282,0.00004377592,0.0002051358,0.8090285,0.001537837,0.00000815834,0.0000111934,0.000156055],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8229982,0.0002966448,0.1757198,0.0006279142,0.00003784549,0.0001512361,0.000001109593,0.00004976581,0.0001174964],"genre_scores_gemma":[0.9840202,0.00002770639,0.01529902,0.0003475684,0.0001231237,0.00001195578,0.00007435455,0.00001670442,0.00007940182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7889358,"threshold_uncertainty_score":0.92131,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2009911194","doi":"10.1016/j.media.2014.07.003","title":"Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study","year":2014,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":158,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"National Heart, Lung, and Blood Institute","keywords":"Segmentation; Computer science; Automation; Artificial intelligence; Computed tomography; Algorithm; Data mining; Machine learning; Radiology; Medicine; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01207245241737646,"gpt":0.3258265763759821,"spread":0.3137541239586056,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00217779,0.000150569,0.0006124961,0.0003319225,0.000146116,0.00003778344,0.0003824384,0.0000639024,0.0000545619],"category_scores_gemma":[0.001091466,0.00008374528,0.0003185625,0.002145392,0.000285788,0.00005383542,0.00009524982,0.000419643,0.000001016694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003950367,"about_ca_system_score_gemma":0.00006384103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006279376,"about_ca_topic_score_gemma":0.00008328503,"domain_scores_codex":[0.9976777,0.0003603775,0.000582111,0.0002883411,0.0008318069,0.0002596267],"domain_scores_gemma":[0.9985811,0.0005035009,0.0001979801,0.0004277354,0.0001366139,0.0001530664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005462175,0.0009536878,0.9749787,0.0001245459,0.001984674,0.00001341295,0.001939786,0.002605286,0.0008540048,0.00004137172,0.003473078,0.01297686],"study_design_scores_gemma":[0.00152763,0.00005789203,0.3228437,0.00006080501,0.001333572,0.000003016738,0.0004726044,0.6734378,0.0001114141,0.00001395835,0.00007880112,0.00005884786],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7431614,0.0001197513,0.2496442,0.005779485,0.0001579279,0.0007961295,0.000003104777,0.0001009518,0.0002369852],"genre_scores_gemma":[0.9957392,0.000006600031,0.003251161,0.000749515,0.000104975,0.0000499596,0.00004871349,0.000016459,0.0000334172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6708325,"threshold_uncertainty_score":0.3415033,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1901606657","doi":"10.1016/j.media.2015.06.009","title":"Abdominal multi-organ segmentation from CT images using conditional shape–location and unsupervised intensity priors","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":156,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Institutes of Health; Ministry of Education, Culture, Sports, Science and Technology; Japan Society for the Promotion of Science; Ontario Council on Graduate Studies, Council of Ontario Universities","keywords":"Prior probability; Segmentation; Artificial intelligence; Pattern recognition (psychology); Computer science; Image segmentation; Computer vision; Bayesian probability","retraction":null,"screen_n_in":null,"score":{"opus":0.02119039426079101,"gpt":0.2817087070582469,"spread":0.2605183127974559,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002405707,0.0001577728,0.0002877754,0.0001805942,0.00008842011,0.00005904251,0.00009736047,0.00004514169,0.0003819495],"category_scores_gemma":[0.0002656168,0.0001555819,0.00007130394,0.0005222601,0.0001571239,0.0004725794,0.00004275172,0.0001897827,0.00002183055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009985031,"about_ca_system_score_gemma":0.0000392737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002232944,"about_ca_topic_score_gemma":0.0000495584,"domain_scores_codex":[0.99883,0.0000455649,0.0002789684,0.0002374005,0.0004217521,0.0001863509],"domain_scores_gemma":[0.9992978,0.00006688117,0.00004578604,0.0001389056,0.0001760262,0.0002745806],"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":[0.0002372171,0.0006713498,0.235475,0.000283706,0.007586901,0.00123682,0.008689699,0.09312543,0.4760271,0.00004918893,0.001928389,0.1746892],"study_design_scores_gemma":[0.0008423335,0.000007094783,0.02082445,0.00002390547,0.0007274886,0.00001484827,0.001199624,0.9639449,0.01205297,0.0001468332,0.00002173847,0.0001938767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.472364,0.0002072717,0.5271481,0.00008712122,0.00004416391,0.00004274739,0.00001486201,0.00007329896,0.00001846814],"genre_scores_gemma":[0.950803,0.00003921345,0.04847467,0.0001798723,0.0001037523,0.000005475364,0.0003652094,0.0000178192,0.00001098307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8708194,"threshold_uncertainty_score":0.6344448,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2803954248","doi":"10.1016/j.media.2018.05.005","title":"Automated comprehensive Adolescent Idiopathic Scoliosis assessment using MVC-Net","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Scoliosis diagnosis and treatment","field":"Medicine","cited_by":156,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"London Health Sciences Centre; Western University","funders":"Government of Ontario","keywords":"Idiopathic scoliosis; Scoliosis; Computer science; Net (polyhedron); Artificial intelligence; Medicine; Physical medicine and rehabilitation; Machine learning; Medical physics; Mathematics; Surgery","retraction":null,"screen_n_in":null,"score":{"opus":0.03776483629591423,"gpt":0.3889479305121381,"spread":0.3511830942162239,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004799868,0.0003473613,0.001135129,0.0006935775,0.0002383899,0.00008990664,0.0002047804,0.0002220594,0.001826309],"category_scores_gemma":[0.0003422159,0.0002681851,0.0006377684,0.002441341,0.0004532354,0.0001149196,0.00016067,0.0003276265,0.000221133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000404471,"about_ca_system_score_gemma":0.0003914011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005227112,"about_ca_topic_score_gemma":0.0001018812,"domain_scores_codex":[0.9962317,0.000236348,0.0007140626,0.0006666198,0.001584846,0.0005664065],"domain_scores_gemma":[0.9974159,0.00005181241,0.0002058777,0.0007008121,0.0008186621,0.0008069079],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000196045,0.004679892,0.9467132,0.0004055115,0.01565494,0.002776033,0.0003610631,0.00002820729,0.009922179,0.0000690974,0.01275596,0.006437889],"study_design_scores_gemma":[0.00261973,0.0005141561,0.7568313,0.001262435,0.012661,0.00006914485,0.0002026713,0.2202376,0.004503111,0.00001054278,0.000744142,0.0003442043],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870583,0.00173257,0.006106416,0.003020475,0.0002804502,0.0005284633,0.00001232278,0.0004913529,0.0007696011],"genre_scores_gemma":[0.9914061,0.0002405673,0.003229761,0.004361009,0.0006163068,0.00004667531,0.00004049508,0.00003572613,0.00002339794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2202094,"threshold_uncertainty_score":0.9999771,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4384701644","doi":"10.1016/j.media.2023.102878","title":"Robotic ultrasound imaging: State-of-the-art and future perspectives","year":2023,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":153,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Process (computing); Artificial intelligence; Teleoperation; Modalities; Human–computer interaction; Data science; Robot","retraction":null,"screen_n_in":null,"score":{"opus":0.01278421280006974,"gpt":0.2920633726128263,"spread":0.2792791598127565,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002103126,0.0002578613,0.001106743,0.0002640366,0.00006428286,0.00005465272,0.000365649,0.0001073487,0.0002135355],"category_scores_gemma":[0.0001803797,0.0001758157,0.0006605174,0.002135279,0.0002033495,0.00003841503,0.00008072978,0.0004298219,0.00006520824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004810904,"about_ca_system_score_gemma":0.00005974743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001133797,"about_ca_topic_score_gemma":0.00002829723,"domain_scores_codex":[0.9985976,0.0000501972,0.0004520352,0.0002916115,0.000397568,0.0002109695],"domain_scores_gemma":[0.9988704,0.00034358,0.00009698477,0.0004783205,0.00005473696,0.0001559287],"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":[3.037692e-7,0.00006711263,0.00008839423,0.007815962,0.007463359,0.00003387653,0.0004726612,0.0008320681,0.000002598539,0.0002742439,0.01208309,0.9708663],"study_design_scores_gemma":[0.00008765137,0.000003638874,0.0002375481,0.001191057,0.01523292,0.00002633718,0.0002483265,0.01311199,0.000001374787,0.0002150832,0.969164,0.0004800884],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000003275702,0.9850162,0.01397693,0.0002417642,0.0001056782,0.0001774179,0.00003310854,0.0001269148,0.0003187157],"genre_scores_gemma":[0.00003067887,0.9986921,0.0004681041,0.00001089695,0.0002114968,0.00004388244,0.00006840134,0.00005402021,0.0004204501],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9703863,"threshold_uncertainty_score":0.7169557,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}