{"meta":{"query_hash":"a0222b839387","filters":{"venue":"Journal of Medical Artificial Intelligence"},"cohort_total":5,"direct_labels_cover":0,"predictions_cover":5,"exported":5,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/a0222b839387","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Medical+Artificial+Intelligence"},"results":[{"id":"W2887167108","doi":"10.21037/jmai.2018.07.02","title":"Machine learning and serious games: opportunities and requirements for detection of mild cognitive impairment","year":2018,"lang":"en","type":"article","venue":"Journal of Medical Artificial Intelligence","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Cognitive impairment; Cognition; Computer science; Psychology; Cognitive psychology; Neuroscience","score_opus":0.08365877540182218,"score_gpt":0.35260653585190294,"score_spread":0.26894776045008073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887167108","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7419005,0.0024392265,0.2247181,0.0090977065,0.00023142117,0.0008123799,0.0030738586,0.001811422,0.015915386],"genre_scores_gemma":[0.9090124,0.0006517861,0.08712024,0.00038898145,0.0001260613,0.0003218617,0.0011038644,0.000046221485,0.0012286619],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977704,0.00076746935,0.000226948,0.00032781714,0.0006932625,0.00021399888],"domain_scores_gemma":[0.98450005,0.010745413,0.0008770706,0.0010692129,0.0020255942,0.00078276073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027847427,0.0007914082,0.00096638454,0.0020611961,0.00051209336,0.002378436,0.0012208162,0.0013505194,0.0028023524],"category_scores_gemma":[0.024498072,0.00033558538,0.0004324146,0.0012349708,0.00066612655,0.0020876266,0.001543844,0.0013425166,0.00081107294],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001519493,0.00096361,0.32404512,0.0006776873,0.00015900786,0.0010941117,0.000872926,0.018340876,0.021602392,0.006650584,0.0058995886,0.6181747],"study_design_scores_gemma":[0.000106079286,0.0017202899,0.27189064,0.00052063935,0.00014183231,0.0045509846,0.0028269647,0.617473,0.021239411,0.06428726,0.015080296,0.00016252659],"about_ca_topic_score_codex":0.00436788,"about_ca_topic_score_gemma":0.0067567276,"teacher_disagreement_score":0.00436788,"about_ca_system_score_codex":0.00051742233,"about_ca_system_score_gemma":0.0008584665,"threshold_uncertainty_score":0.014727294},"labels":[],"label_agreement":null},{"id":"W2909847169","doi":"10.21037/jmai.2019.01.01","title":"Promises and limitations of deep learning for medical image segmentation","year":2019,"lang":"en","type":"article","venue":"Journal of Medical Artificial Intelligence","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Artificial intelligence; Deep learning; Computer vision; Segmentation; Image (mathematics); Computer science","score_opus":0.043449062245623435,"score_gpt":0.36256073053461263,"score_spread":0.3191116682889892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909847169","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018667359,0.29730195,0.5266406,0.12073264,0.0024939377,0.000120036406,0.0013313672,0.0020875898,0.03062446],"genre_scores_gemma":[0.41408455,0.2231574,0.31011987,0.018687822,0.010233207,0.000350455,0.001957566,0.00074618525,0.0206629],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99854124,0.000480788,0.00006250807,0.00022909905,0.00060142303,0.000085030275],"domain_scores_gemma":[0.99152243,0.005688167,0.0002639368,0.0007819608,0.0013928915,0.00035060543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054572863,0.0013845915,0.0010905914,0.0013130264,0.00037008323,0.0024632283,0.0026650908,0.0032440864,0.005422585],"category_scores_gemma":[0.013025822,0.00065294874,0.00070446543,0.0011477951,0.0028095772,0.0052609397,0.0026682152,0.004932541,0.0030350764],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000507994,0.0001400489,0.0020520438,0.0012944805,0.00021445767,0.000091726644,0.00015608333,0.059562065,0.0038061887,0.14929457,0.037570104,0.74531025],"study_design_scores_gemma":[0.00006473583,0.00023194759,0.0017828556,0.0011989691,0.00008060763,0.0003015355,0.00012171412,0.48586744,0.0055625495,0.3832791,0.12137347,0.00013516577],"about_ca_topic_score_codex":0.0036529289,"about_ca_topic_score_gemma":0.0025286963,"teacher_disagreement_score":0.0054572863,"about_ca_system_score_codex":0.0016377596,"about_ca_system_score_gemma":0.0013302155,"threshold_uncertainty_score":0.028861225},"labels":[],"label_agreement":null},{"id":"W2974630971","doi":"10.21037/jmai.2019.09.04","title":"Artificial intelligence and colorectal polyp detection","year":2019,"lang":"en","type":"article","venue":"Journal of Medical Artificial Intelligence","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver General Hospital; University of British Columbia","funders":"","keywords":"Colorectal cancer; Colonoscopy; Incidence (geometry); Medicine; Cancer; Cause of death; Internal medicine; Oncology; General surgery; Disease","score_opus":0.028525185784608133,"score_gpt":0.33072993466385864,"score_spread":0.30220474887925053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974630971","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09459852,0.5144366,0.076959684,0.09494857,0.004366697,0.00014542327,0.0014707893,0.00044764145,0.21262611],"genre_scores_gemma":[0.8134335,0.13271846,0.024629066,0.004806595,0.0031836834,0.000089554844,0.00089481473,0.000037104073,0.020207228],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992035,0.00033096405,0.000039228526,0.00012118781,0.00025115465,0.000053913514],"domain_scores_gemma":[0.9960663,0.0028776194,0.00040569773,0.00011137085,0.00042147966,0.000117536634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008280949,0.00032202914,0.00031570825,0.0012559084,0.00033819024,0.0019311293,0.00034724086,0.0010852603,0.003496299],"category_scores_gemma":[0.0076049296,0.00012226013,0.00029099363,0.0013400197,0.0008206911,0.0009970184,0.00050092593,0.0012293579,0.0008103132],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020847115,0.00028088677,0.042186856,0.0012650632,0.0002782348,0.0006707989,0.00031928846,0.018557109,0.0009605722,0.19235888,0.05300899,0.6899049],"study_design_scores_gemma":[0.000034940964,0.0003627699,0.06682571,0.001399734,0.00014118092,0.0020964479,0.0006162867,0.08310286,0.0010297961,0.6887346,0.15554197,0.00011380224],"about_ca_topic_score_codex":0.0031575179,"about_ca_topic_score_gemma":0.002088472,"teacher_disagreement_score":0.003496299,"about_ca_system_score_codex":0.00077663927,"about_ca_system_score_gemma":0.00062225095,"threshold_uncertainty_score":0.011696279},"labels":[],"label_agreement":null},{"id":"W4381987696","doi":"10.21037/jmai-23-10","title":"Approximating femoral neck bone mineral density from hand, knee, and pelvis X-rays using deep learning","year":2023,"lang":"en","type":"article","venue":"Journal of Medical Artificial Intelligence","topic":"Body Composition Measurement Techniques","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Activation Laboratories; University of Toronto; AUG Signals (Canada); Sunnybrook Health Science Centre; Health Sciences Centre","funders":"","keywords":"Pelvis; Bone mineral; Femoral neck; Medicine; Orthodontics; Geology; Radiology; Anatomy; Computer science; Osteoporosis; Internal medicine","score_opus":0.0887147532116295,"score_gpt":0.34555590967411615,"score_spread":0.2568411564624866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381987696","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54679316,0.00091284484,0.4461002,0.00037242754,0.00005949471,0.00009935023,0.0013489918,0.0017913998,0.0025220227],"genre_scores_gemma":[0.9407169,0.00021630924,0.0561985,0.00010974916,0.000021133117,0.000077200806,0.0011536189,0.000031498585,0.001475065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982893,0.000033186247,0.000010054353,0.000056252808,0.000038095855,0.000033488534],"domain_scores_gemma":[0.99961025,0.0001856159,0.00006621047,0.000029603472,0.00008448234,0.000023862585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004902511,0.00065159384,0.00034250724,0.0006120918,0.00014048361,0.00049096526,0.00068552076,0.00056071824,0.001087547],"category_scores_gemma":[0.001867398,0.0002374267,0.0004328107,0.000394428,0.0002327707,0.0003145216,0.00057611155,0.0005771561,0.00034896436],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006053242,0.00043938853,0.071215644,0.00018901957,0.00022648336,0.0002558984,0.00012866911,0.6354562,0.011300202,0.0010249293,0.0034475785,0.27571064],"study_design_scores_gemma":[0.000014203751,0.000048491864,0.007225984,0.000017908862,0.000017312583,0.000079691206,0.000017875025,0.9887683,0.0025554623,0.0008810899,0.0003649558,0.000008766563],"about_ca_topic_score_codex":0.017381087,"about_ca_topic_score_gemma":0.016242398,"teacher_disagreement_score":0.017381087,"about_ca_system_score_codex":0.0007368055,"about_ca_system_score_gemma":0.0007502214,"threshold_uncertainty_score":0.034559786},"labels":[],"label_agreement":null},{"id":"W4404456084","doi":"10.21037/jmai-24-148","title":"The long road ahead: navigating obstacles and building bridges for clinical integration of artificial intelligence technologies","year":2024,"lang":"en","type":"article","venue":"Journal of Medical Artificial Intelligence","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial intelligence; Computer science; Engineering; Architectural engineering; Transport engineering; Construction engineering","score_opus":0.29487668016752616,"score_gpt":0.5312757897015817,"score_spread":0.23639910953405557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404456084","genre_codex":"review","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037386776,0.5515795,0.03934066,0.38596702,0.0054508937,0.0005100353,0.00012939866,0.00025508954,0.013028753],"genre_scores_gemma":[0.12395141,0.55152375,0.16543502,0.14559251,0.0059977844,0.0025371588,0.0005449555,0.00041486663,0.00400251],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.91331357,0.06092796,0.008401786,0.0037855122,0.011188613,0.0023825124],"domain_scores_gemma":[0.71839166,0.23270482,0.011799111,0.008176836,0.023901392,0.0050261244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1133114,0.0011457404,0.002461734,0.0048717405,0.003893461,0.021231959,0.005779838,0.01105081,0.009013462],"category_scores_gemma":[0.17226438,0.0012003885,0.003156181,0.0041565895,0.010141957,0.04434175,0.014489138,0.018878615,0.0027329377],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023918401,0.0002577841,0.0021349797,0.050543293,0.0006627831,0.00081743376,0.011082206,0.002114981,0.0008335304,0.2724,0.05984236,0.5990715],"study_design_scores_gemma":[0.00007785332,0.0003034293,0.0014453336,0.12164198,0.00047577103,0.00077093835,0.013699425,0.0017243506,0.000977841,0.2798684,0.5788632,0.0001515208],"about_ca_topic_score_codex":0.002631501,"about_ca_topic_score_gemma":0.003910395,"teacher_disagreement_score":0.1133114,"about_ca_system_score_codex":0.006330373,"about_ca_system_score_gemma":0.04629149,"threshold_uncertainty_score":0.59925514},"labels":[],"label_agreement":null}]}