{"meta":{"query_hash":"f5d033aa9c07","filters":{"venue":"Mayo Clinic Proceedings Digital Health"},"cohort_total":21,"direct_labels_cover":0,"predictions_cover":21,"exported":21,"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/f5d033aa9c07","api":"https://metacan.xera.ac/api/v1/cohort?venue=Mayo+Clinic+Proceedings+Digital+Health"},"results":[{"id":"W4378072082","doi":"10.1016/j.mcpdig.2023.02.008","title":"Screening for Impaired Glucose Homeostasis: A Novel Metric of Glycemic Control","year":2023,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings Digital Health","topic":"Diabetes Management and Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Mitacs; University of Ontario Institute of Technology","keywords":"Prediabetes; Glucose homeostasis; Glycemic; Medicine; Glycated hemoglobin; Internal medicine; Impaired glucose tolerance; Type 2 diabetes; Endocrinology; Diabetes mellitus; Blood sugar regulation; Glucose tolerance test; Homeostasis; Impaired fasting glucose; Insulin resistance","score_opus":0.0855893332428475,"score_gpt":0.38386461067984834,"score_spread":0.29827527743700083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378072082","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9705119,0.0021900248,0.0027867772,0.009673703,0.00039024695,0.0060125673,0.0005600146,0.00082833145,0.0070464234],"genre_scores_gemma":[0.9944953,0.00020802997,0.0005517738,0.00076507375,0.00019191563,0.000097566444,0.00012269936,0.000059837177,0.0035078062],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99719346,0.0000046393984,0.0008454411,0.00045503365,0.00060417264,0.0008972407],"domain_scores_gemma":[0.99830115,0.00043271267,0.0003751589,0.00016465673,0.00034318044,0.0003831549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015741002,0.00020964099,0.00076197594,0.00092460925,0.00011708543,0.00009702705,0.0002338976,0.00010414275,0.000023350556],"category_scores_gemma":[0.0016377188,0.00017927987,0.00029557996,0.0021017434,0.000115104885,0.00033544103,0.00014043455,0.00023252335,0.000083757266],"study_design_candidate":"design_other","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":[0.0070061916,0.0012546813,0.035287388,0.008999373,0.00067213783,0.0000067800543,0.00049292337,0.0000025330544,0.00027437852,0.0007418443,0.0486063,0.89665544],"study_design_scores_gemma":[0.21772335,0.08898176,0.3312023,0.010775796,0.0013313981,0.000059817117,0.02177516,0.08973428,0.0019315335,0.008026354,0.2250418,0.0034164253],"about_ca_topic_score_codex":0.000009768297,"about_ca_topic_score_gemma":5.6010254e-7,"teacher_disagreement_score":0.893239,"about_ca_system_score_codex":0.000097873046,"about_ca_system_score_gemma":0.00025812103,"threshold_uncertainty_score":0.73108214},"labels":[],"label_agreement":null},{"id":"W4378191612","doi":"10.1016/j.mcpdig.2023.04.003","title":"Health Inequalities in Rural and Urban Bangladesh: The Implications of Digital Health","year":2023,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings Digital Health","topic":"Healthcare Systems and Reforms","field":"Economics, Econometrics and Finance","cited_by":15,"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; Impact","funders":"","keywords":"Inequality; Socioeconomics; Geography; Sociology; Economic growth; Economics; Mathematics","score_opus":0.08490189488207864,"score_gpt":0.32647996361466985,"score_spread":0.24157806873259122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378191612","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8184588,0.006887295,0.000056681627,0.15682411,0.00059300056,0.0019656145,0.0027093298,0.0002518236,0.012253344],"genre_scores_gemma":[0.99395734,0.002037723,0.000018823312,0.0020351992,0.0001624104,0.0000742718,0.00012083233,0.000042565865,0.001550831],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99590194,0.00001526858,0.002566225,0.00048496353,0.000107266256,0.00092435797],"domain_scores_gemma":[0.99774706,0.000119216376,0.0014139839,0.0002555417,0.00006673051,0.00039745704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026956485,0.00023530595,0.0010204342,0.00042582353,0.00030091486,0.00024315031,0.00030781477,0.00011406203,0.00000977639],"category_scores_gemma":[0.0002832773,0.00018503779,0.00012491652,0.0010843211,0.0001756168,0.00074146566,0.00015874376,0.00035678837,0.00014766962],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021731306,0.0001411321,0.76249856,0.0020309614,0.000025373032,3.4813058e-7,0.010498894,5.03611e-7,8.072007e-8,0.10914018,0.010804435,0.10483781],"study_design_scores_gemma":[0.0010708452,0.0013444873,0.8505142,0.00064469886,6.2045274e-7,0.000029366476,0.015750097,0.00015228934,3.775703e-7,0.060102776,0.069974266,0.0004159844],"about_ca_topic_score_codex":0.003377368,"about_ca_topic_score_gemma":0.00015792146,"teacher_disagreement_score":0.17549855,"about_ca_system_score_codex":0.00037992315,"about_ca_system_score_gemma":0.00048176924,"threshold_uncertainty_score":0.7545622},"labels":[],"label_agreement":null},{"id":"W4380360840","doi":"10.1016/j.mcpdig.2023.05.004","title":"Learning to Fake It: Limited Responses and Fabricated References Provided by ChatGPT for Medical Questions","year":2023,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings Digital Health","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":172,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Children's Hospital; Université de Montréal; Cegep de Saint Hyacinthe; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Fake news; Medical education; Psychology; Computer science; Internet privacy; Data science; Medicine","score_opus":0.19701166210050622,"score_gpt":0.48584274596377186,"score_spread":0.2888310838632656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380360840","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8319949,0.000167481,0.00013045697,0.1646352,0.00027505198,0.0014657982,0.000049846243,0.0004745385,0.00080671126],"genre_scores_gemma":[0.98280555,0.00076727994,0.00018710278,0.0059890514,0.00028947246,0.00032514456,0.00022138708,0.000036613164,0.00937837],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974673,0.000025288888,0.00091568776,0.0005468862,0.00043238388,0.0006124665],"domain_scores_gemma":[0.99720365,0.0011105553,0.00022247694,0.00010334396,0.0004623885,0.00089760416],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0016467287,0.00018265717,0.00041019073,0.0003028235,0.0003844647,0.00014592368,0.00012325567,0.00020451011,0.00004223544],"category_scores_gemma":[0.015927225,0.00016393102,0.000059833063,0.0009146145,0.000098755,0.0002585562,0.00006654083,0.00040152503,0.00021980783],"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":[0.0013560577,0.0003249446,0.055698518,0.0012261784,0.000037869584,0.0000034762468,0.011203703,2.7169008e-7,0.00009308097,0.00044880118,0.2189863,0.71062076],"study_design_scores_gemma":[0.001001439,0.021945408,0.040570647,0.0043957704,0.00008022798,0.00015536396,0.07679661,0.005962503,0.001067409,0.0066161775,0.84033793,0.0010705123],"about_ca_topic_score_codex":0.00023431725,"about_ca_topic_score_gemma":0.000033523058,"teacher_disagreement_score":0.70955026,"about_ca_system_score_codex":0.00013461737,"about_ca_system_score_gemma":0.0010257408,"threshold_uncertainty_score":0.992362},"labels":[],"label_agreement":null},{"id":"W4382310384","doi":"10.1016/j.mcpdig.2023.04.002","title":"Personalized Health Care in a Data-Driven Era: A Post–COVID-19 Retrospective","year":2023,"lang":"en","type":"review","venue":"Mayo Clinic Proceedings Digital Health","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Canterbury","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Health care; Retrospective cohort study; Pandemic; Medicine; Data science; Computer science; Virology; Economic growth; Internal medicine; Economics; Infectious disease (medical specialty); Disease","score_opus":0.2838512539350578,"score_gpt":0.565748523400401,"score_spread":0.28189726946534327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382310384","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000029346907,0.9480463,0.000015803622,0.011317645,0.002704768,0.02073003,0.010293999,0.0016483921,0.0052137054],"genre_scores_gemma":[0.000065808585,0.96391886,0.00018266652,0.012611454,0.0022452588,0.0033956808,0.0075241774,0.0007513543,0.009304747],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9755248,0.0017151702,0.010443525,0.0044951933,0.0018069643,0.006014391],"domain_scores_gemma":[0.98086786,0.003840971,0.0087231295,0.0019352577,0.00085852016,0.0037742397],"candidate_categories":["metaresearch","metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.012576661,0.0018802242,0.010660454,0.0019182408,0.0021182299,0.00024400608,0.003270957,0.0020367466,0.0002933593],"category_scores_gemma":[0.011171385,0.0017112498,0.0009061571,0.00428725,0.0003144328,0.0011680672,0.0021810494,0.010083879,0.005628024],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00018790136,0.00022712546,0.0014022136,0.4781196,0.00024090626,0.00004062277,0.018051568,4.2597573e-8,1.7425471e-9,0.0013134833,0.14873324,0.3516833],"study_design_scores_gemma":[0.0022119703,0.0016000345,0.00009221278,0.06797296,0.00008065059,0.00006443734,0.013888033,0.000018356208,3.4486206e-10,0.00024927515,0.91279125,0.0010307943],"about_ca_topic_score_codex":0.0067858137,"about_ca_topic_score_gemma":0.011587994,"teacher_disagreement_score":0.76405805,"about_ca_system_score_codex":0.03839103,"about_ca_system_score_gemma":0.12984419,"threshold_uncertainty_score":0.9998281},"labels":[],"label_agreement":null},{"id":"W4387721637","doi":"10.1016/j.mcpdig.2023.08.005","title":"Acoustic Analysis and Prediction of Type 2 Diabetes Mellitus Using Smartphone-Recorded Voice Segments","year":2023,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings Digital Health","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Medicine; Body mass index; Type 2 Diabetes Mellitus; Audiology; Diabetes mellitus; Sample entropy; Internal medicine; Mathematics; Statistics; Endocrinology","score_opus":0.043848212849930214,"score_gpt":0.3345118816962241,"score_spread":0.29066366884629385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387721637","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9974011,0.00042023836,0.00005586405,0.00046467295,0.00022292892,0.00038178518,0.00008609675,0.00016964765,0.00079762517],"genre_scores_gemma":[0.99739194,0.0010490023,0.0001984892,0.00037430692,0.00008577488,0.0000070626606,0.00015091417,0.000028635011,0.00071388757],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99813604,0.0000075284397,0.00067140267,0.00039300002,0.00034929876,0.0004427255],"domain_scores_gemma":[0.9989749,0.00010558081,0.0003010344,0.00012688257,0.00023276106,0.00025884158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005210566,0.00017704781,0.00062532077,0.0004911433,0.00010156148,0.00005347151,0.00007185956,0.000109590896,0.000031228155],"category_scores_gemma":[0.00044222557,0.00016541583,0.00013282886,0.002214731,0.00009304961,0.0003212586,0.0000683442,0.00017886903,0.000057655092],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016699157,0.00022994359,0.970094,0.001442246,0.0006071696,0.0000022640927,0.00062739203,0.000009452403,0.00064862386,0.00000343953,0.0020657014,0.024102723],"study_design_scores_gemma":[0.0034257395,0.0033452038,0.9422284,0.0008030322,0.0017195127,0.000013239841,0.004445178,0.040769357,0.0004558414,0.0005337558,0.0018531517,0.0004075998],"about_ca_topic_score_codex":0.00006358631,"about_ca_topic_score_gemma":0.000010864727,"teacher_disagreement_score":0.040759906,"about_ca_system_score_codex":0.000085661624,"about_ca_system_score_gemma":0.0001927242,"threshold_uncertainty_score":0.6745462},"labels":[],"label_agreement":null},{"id":"W4388962525","doi":"10.1016/j.mcpdig.2023.10.005","title":"Complexities and Questions Toward Artificial Intelligence for Diagnostic Support in Virtual Primary Care","year":2023,"lang":"en","type":"editorial","venue":"Mayo Clinic Proceedings Digital Health","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trillium Health Centre; Western University","funders":"","keywords":"Primary care; Sociotechnical system; Health care; Artificial intelligence; Medical diagnosis; Psychology; Digital health; Medical education; Medicine; Computer science; Family medicine; Political science; Pathology","score_opus":0.20451744686319723,"score_gpt":0.46243308196602306,"score_spread":0.25791563510282584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388962525","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013130963,0.001452092,0.0007579861,0.010638817,0.9629642,0.005851832,0.0026988017,0.0006454354,0.001859868],"genre_scores_gemma":[0.26980576,0.006565961,0.0007051755,0.0014355615,0.7039261,0.0017573067,0.0130258305,0.00037151115,0.0024067732],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99526364,0.000018802873,0.0021252022,0.0009775789,0.00068177737,0.00093297864],"domain_scores_gemma":[0.9903493,0.0071210763,0.00060344045,0.00021435534,0.0011442463,0.00056760584],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010944096,0.00046612162,0.0011688696,0.00050236454,0.00025463843,0.00034713603,0.00022706445,0.00081563514,0.00002689892],"category_scores_gemma":[0.015501529,0.0004883788,0.00018040645,0.0004770872,0.0003547423,0.00045459913,0.00013702843,0.0012314953,0.0001987676],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005415982,0.00030258397,0.0012421989,0.011051709,0.00003067067,0.000009944072,0.010518335,0.0000010428399,3.1157796e-7,0.0014214421,0.6575152,0.31736493],"study_design_scores_gemma":[0.00036465572,0.017213814,0.0015653853,0.011479451,0.00020766108,0.000032113003,0.107658446,0.00020206692,0.00004565966,0.07125665,0.7882853,0.0016887727],"about_ca_topic_score_codex":0.0006691587,"about_ca_topic_score_gemma":0.00032966732,"teacher_disagreement_score":0.31567615,"about_ca_system_score_codex":0.0011491451,"about_ca_system_score_gemma":0.004831808,"threshold_uncertainty_score":0.9997568},"labels":[],"label_agreement":null},{"id":"W4391089366","doi":"10.1016/j.mcpdig.2023.07.004","title":"From 0-50 in Pandemic, and Then Back? A Case Study of Virtual Care in Ontario Pre–COVID-19, During, and Post–COVID-19","year":2024,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings Digital Health","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"Western University","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Viral therapy; Betacoronavirus; Virology; Medicine; Outbreak","score_opus":0.06575686006989293,"score_gpt":0.4151748522649675,"score_spread":0.3494179921950745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391089366","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9930323,0.0012397359,0.000003010746,0.0024592355,0.00011647195,0.002684172,0.00013398788,0.0001144116,0.00021670356],"genre_scores_gemma":[0.9958368,0.0002092221,0.000080454956,0.0032543824,0.000121831865,0.00009921621,0.00009994633,0.000034349938,0.00026382686],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971285,0.00003140146,0.0012961071,0.00074075616,0.00033485924,0.00046836544],"domain_scores_gemma":[0.99827045,0.00039115458,0.0002483393,0.000139076,0.000066748384,0.0008842514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072996947,0.00026549882,0.0007009016,0.0004949714,0.00009230929,0.00007715965,0.00007805224,0.00012681591,0.00013878073],"category_scores_gemma":[0.0005892988,0.00023442364,0.00004088539,0.0003308673,0.00009632043,0.00040961357,0.00014296845,0.0006079778,0.0000049056835],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084575446,0.000393991,0.7919196,0.0030053142,0.000034551416,0.00053379656,0.18120173,1.6738632e-7,0.0000067657766,0.00001145837,0.00036929053,0.02167758],"study_design_scores_gemma":[0.01302126,0.012097235,0.64930314,0.0006809086,0.00006276059,0.0014497797,0.31422648,0.000032833614,0.000002639555,0.00028056823,0.008586714,0.00025569522],"about_ca_topic_score_codex":0.30338565,"about_ca_topic_score_gemma":0.26564872,"teacher_disagreement_score":0.14261648,"about_ca_system_score_codex":0.0012436784,"about_ca_system_score_gemma":0.0027354227,"threshold_uncertainty_score":0.95595187},"labels":[],"label_agreement":null},{"id":"W4392690442","doi":"10.1016/j.mcpdig.2024.01.011","title":"Exercise Testing and Artificial Intelligence as Allies in Improving the Detection and Diagnosis of Long QT Syndrome","year":2024,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings Digital Health","topic":"Cardiac electrophysiology and arrhythmias","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":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Long QT syndrome; Medicine; Battle; Scopus; Artificial intelligence; Psychology; Cardiology; Internal medicine; QT interval; MEDLINE; Political science; Law; Computer science; History","score_opus":0.0320226909708765,"score_gpt":0.31710056203700976,"score_spread":0.28507787106613325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392690442","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9937185,0.004436666,0.000030687297,0.0009882681,0.000114462266,0.00043718144,0.0000042426386,0.00005958383,0.0002104323],"genre_scores_gemma":[0.9982767,0.0013426124,0.000049722326,0.00013221537,0.00008808541,0.00004763769,0.0000013062155,0.000015938844,0.000045786743],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988643,0.00000698665,0.00044220372,0.0003205892,0.00011194417,0.0002539997],"domain_scores_gemma":[0.99916816,0.0005301178,0.00010315192,0.000058843052,0.00005317481,0.00008655248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048953906,0.00012388696,0.00033561903,0.00011893587,0.00009169791,0.00008432445,0.00003644835,0.000078274454,0.0000039869565],"category_scores_gemma":[0.00081197696,0.000092428985,0.000048863272,0.00036609656,0.00019522726,0.00024149672,0.00007106162,0.0003519928,0.000008535088],"study_design_candidate":"design_other","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":[0.00015243585,0.00004699553,0.014853551,0.0014996803,0.000018311037,0.00012571522,0.0007349029,5.586451e-7,0.00018402244,0.0001960925,0.000008441158,0.9821793],"study_design_scores_gemma":[0.0013918949,0.023133324,0.7716097,0.027187873,0.00053751294,0.026669206,0.01344405,0.017237013,0.02281491,0.09400777,0.00033093206,0.0016358095],"about_ca_topic_score_codex":0.00010694671,"about_ca_topic_score_gemma":0.000011956651,"teacher_disagreement_score":0.9805435,"about_ca_system_score_codex":0.000047670124,"about_ca_system_score_gemma":0.000120663964,"threshold_uncertainty_score":0.37691444},"labels":[],"label_agreement":null},{"id":"W4393861178","doi":"10.1016/j.mcpdig.2024.03.001","title":"Empowering Patients in the Digital Age: New Framework to Measure and Improve Patient Digital Experiences","year":2024,"lang":"en","type":"review","venue":"Mayo Clinic Proceedings Digital Health","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Scopus; eHealth; Digital health; Health care; Empowerment; Telemedicine; Nursing; Psychology; Medicine; Library science; Political science; MEDLINE; Computer science","score_opus":0.07642101663831109,"score_gpt":0.4390803058657039,"score_spread":0.3626592892273928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393861178","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025569033,0.95355725,0.000032799788,0.003070943,0.0017802451,0.009823953,0.0005053736,0.00030602378,0.0053543695],"genre_scores_gemma":[0.18473586,0.804435,0.00024912265,0.0058232145,0.0016775384,0.0010704072,0.00096614496,0.00027710275,0.0007655632],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.994364,0.000016667738,0.0023643884,0.0011448418,0.001102259,0.0010078116],"domain_scores_gemma":[0.99760205,0.00034206032,0.0007168587,0.00030360156,0.000102368096,0.0009330735],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005101903,0.00076877594,0.0019885243,0.00048652125,0.00014196376,0.0014334416,0.0003588113,0.0003297321,0.000017704828],"category_scores_gemma":[0.0018518677,0.0004907299,0.00031626836,0.0012097696,0.00011308191,0.00079018116,0.0003072343,0.001354713,0.00016280808],"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":[0.000051692696,0.00019441803,0.0013857711,0.010839125,0.000048212245,0.000026280173,0.0154266525,1.1929103e-9,2.1000879e-9,0.000043916,0.013515028,0.9584689],"study_design_scores_gemma":[0.00064008957,0.004425639,0.00027935533,0.021489598,0.00013546506,0.00009111342,0.013650178,5.7891947e-7,5.1515663e-8,0.0003030657,0.95855343,0.00043140835],"about_ca_topic_score_codex":0.000043276224,"about_ca_topic_score_gemma":0.000005726255,"teacher_disagreement_score":0.9580375,"about_ca_system_score_codex":0.0004318701,"about_ca_system_score_gemma":0.0011982871,"threshold_uncertainty_score":0.9997544},"labels":[],"label_agreement":null},{"id":"W4396557964","doi":"10.1016/j.mcpdig.2024.04.006","title":"The Development and Performance of a Machine-Learning Based Mobile Platform for Visually Determining the Etiology of 5 Penile Diseases","year":2024,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings Digital Health","topic":"Sexual function and dysfunction studies","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Etiology; Computer science; Human–computer interaction; Medicine; Artificial intelligence; Pathology","score_opus":0.04776468517389553,"score_gpt":0.35491488756722694,"score_spread":0.3071502023933314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396557964","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9964022,0.0012005745,0.00012586817,0.0004514464,0.00024395896,0.00063925085,0.000020168403,0.000068578076,0.000847933],"genre_scores_gemma":[0.99798477,0.00013408599,0.00013361016,0.00033337588,0.000062196326,0.00014354211,0.000023657978,0.000015844022,0.0011689184],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99887824,0.0000044868434,0.00054968393,0.00019817687,0.00016785205,0.00020158404],"domain_scores_gemma":[0.99854165,0.0009437522,0.00019423402,0.00005309809,0.00018936704,0.000077866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005506064,0.00011523739,0.00028146533,0.000065769826,0.00030655804,0.000045372708,0.0000557761,0.000033512108,0.0000098686505],"category_scores_gemma":[0.0004954913,0.000062394305,0.00006378598,0.00014685755,0.0001605161,0.00011779596,0.000053523647,0.0001498423,0.000004525666],"study_design_candidate":"observational","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":[0.0025481328,0.00021736806,0.52080876,0.0048868894,0.00014229222,3.8076752e-7,0.003090981,0.0000040778523,0.000012640476,0.00033469664,0.0024249512,0.46552882],"study_design_scores_gemma":[0.004861147,0.02313882,0.39674422,0.001362842,0.00019480789,0.000043554086,0.013331733,0.040316842,0.0004092388,0.00014178408,0.5190327,0.00042231317],"about_ca_topic_score_codex":0.0000017975638,"about_ca_topic_score_gemma":0.000001537357,"teacher_disagreement_score":0.51660776,"about_ca_system_score_codex":0.000039812534,"about_ca_system_score_gemma":0.00032035416,"threshold_uncertainty_score":0.25443658},"labels":[],"label_agreement":null},{"id":"W4398201186","doi":"10.1016/j.mcpdig.2024.05.006","title":"Transforming Health Care With Artificial Intelligence: Redefining Medical Documentation","year":2024,"lang":"en","type":"review","venue":"Mayo Clinic Proceedings Digital Health","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Documentation; Health care; Medicine; Nursing; Medical education; Computer science; Political science","score_opus":0.2534232255429803,"score_gpt":0.5287201028819126,"score_spread":0.2752968773389323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398201186","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000058533955,0.9816045,0.00045029513,0.007399788,0.0014435592,0.003517503,0.00009773015,0.00042940886,0.00499868],"genre_scores_gemma":[0.0025463358,0.9909252,0.00045803995,0.0019269314,0.0017909204,0.00038134147,0.0013125835,0.00020917981,0.00044948095],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9920513,0.00004594454,0.0039255735,0.0013268515,0.0014330026,0.001217332],"domain_scores_gemma":[0.996512,0.00032754822,0.0011227535,0.00028850557,0.00040071973,0.0013485004],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017708571,0.0008048196,0.002813628,0.00067974266,0.00043801658,0.0005455035,0.0003347541,0.00070036383,0.00022123093],"category_scores_gemma":[0.00046259756,0.00059806113,0.0005820595,0.0014790153,0.00024444127,0.0005832536,0.00007557658,0.002227506,0.0011245682],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077996854,0.00012938058,0.000016730626,0.09685895,0.00009554859,0.00001734348,0.0062865107,2.0717739e-8,3.1927163e-9,0.0016700231,0.0019159965,0.8929315],"study_design_scores_gemma":[0.00004364829,0.0037107004,5.337589e-7,0.1188689,0.00044008304,0.0006889021,0.027388828,0.00001724376,0.0000022049303,0.002023886,0.8462235,0.0005915759],"about_ca_topic_score_codex":0.0002607895,"about_ca_topic_score_gemma":0.00007865344,"teacher_disagreement_score":0.89233994,"about_ca_system_score_codex":0.002071527,"about_ca_system_score_gemma":0.015743041,"threshold_uncertainty_score":0.99965316},"labels":[],"label_agreement":null},{"id":"W4401477558","doi":"10.1016/j.mcpdig.2024.08.002","title":"Virtual Reality Videos for Symptom Management in Hospice and Palliative Care","year":2024,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings Digital Health","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institutes of Health","keywords":"Virtual reality; Palliative care; Hospice care; Nursing; Medicine; Augmented reality; Psychology; Computer science; Human–computer interaction","score_opus":0.05150807240217714,"score_gpt":0.37766374560789157,"score_spread":0.3261556732057144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401477558","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34957302,0.0053909994,0.50304365,0.06134864,0.0014048404,0.0102761565,0.0008352341,0.0020729878,0.0660545],"genre_scores_gemma":[0.9959472,0.00042031356,0.0018962246,0.0009790226,0.000058016118,0.00020341724,0.00002061276,0.000015674563,0.00045951494],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99813527,0.000006860143,0.0005086632,0.0006913579,0.00022703812,0.00043081256],"domain_scores_gemma":[0.99917424,0.00020318183,0.000109946865,0.0001862071,0.000095362746,0.00023105861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005494367,0.00017394949,0.0002531092,0.00015029461,0.00011898178,0.00082687504,0.00039258946,0.00006786864,0.0000014688244],"category_scores_gemma":[0.000100256686,0.00016173715,0.00006124268,0.00056462246,0.000057179997,0.0010971493,0.0002666168,0.00016732296,0.00002818865],"study_design_candidate":"design_other","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":[0.000019704465,0.00008669238,0.0008204787,0.00097549276,0.000024151668,0.000001977717,0.006892363,0.0000011219253,7.1794716e-7,0.32247603,0.0035972495,0.66510403],"study_design_scores_gemma":[0.0041967966,0.007492536,0.16403955,0.0035366924,0.000049329712,0.000023501383,0.02536333,0.0692358,0.000079048885,0.22636871,0.49759054,0.0020241747],"about_ca_topic_score_codex":0.000049277787,"about_ca_topic_score_gemma":0.00001661808,"teacher_disagreement_score":0.66307986,"about_ca_system_score_codex":0.0002735796,"about_ca_system_score_gemma":0.00016901277,"threshold_uncertainty_score":0.7973575},"labels":[],"label_agreement":null},{"id":"W4403267015","doi":"10.1016/j.mcpdig.2024.09.003","title":"Deep Learning Model for Predicting Neurodevelopmental Outcome in Very Preterm Infants Using Cerebral Ultrasound","year":2024,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings Digital Health","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Nova Scotia Department of Agriculture; Nova Scotia Hospital; Nova Scotia Health Authority; Izaak Walton Killam Health Centre; Dalhousie University","funders":"IWK Health Centre; University of Toronto; Dalhousie University; RSNA Research and Education Foundation","keywords":"Receiver operating characteristic; Medicine; Area under the curve; Cohort; Pediatrics; Internal medicine","score_opus":0.058731593274850134,"score_gpt":0.355499845181139,"score_spread":0.29676825190628886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403267015","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99282247,0.00041508008,0.0020490743,0.00039905478,0.00051732344,0.001043986,0.00005049851,0.00031968296,0.0023828065],"genre_scores_gemma":[0.99407434,0.00003618953,0.0028134244,0.0011293513,0.00019949528,0.00004784843,0.00007905641,0.00008189237,0.00153839],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971517,0.000008596359,0.0010524909,0.000734555,0.00027744385,0.0007752564],"domain_scores_gemma":[0.9990691,0.0003223203,0.00018207206,0.000085564665,0.00006747437,0.00027350345],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006932286,0.00029531147,0.000592842,0.00028329057,0.00015593102,0.00017865993,0.00013183898,0.00016845245,0.000012427904],"category_scores_gemma":[0.0009738047,0.00027709955,0.00017088621,0.00034017867,0.00010331329,0.00080195407,0.0001339042,0.0007475005,0.000026690715],"study_design_candidate":"observational","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":[0.0006830434,0.00017943274,0.92765266,0.004016395,0.00005093571,0.000062431725,0.0049832673,0.00008978756,0.00027393817,0.00018940818,0.00011565359,0.06170307],"study_design_scores_gemma":[0.003600904,0.001901922,0.10305568,0.0019717102,0.000056162353,0.0019014708,0.0017757316,0.8814884,0.000051529176,0.0025597883,0.00093690486,0.0006998137],"about_ca_topic_score_codex":0.000012013594,"about_ca_topic_score_gemma":0.000004274141,"teacher_disagreement_score":0.8813986,"about_ca_system_score_codex":0.00032747685,"about_ca_system_score_gemma":0.00035991642,"threshold_uncertainty_score":0.9999681},"labels":[],"label_agreement":null},{"id":"W4403901714","doi":"10.1016/j.mcpdig.2024.07.005","title":"Developing a Research Center for Artificial Intelligence in Medicine","year":2024,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings Digital Health","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Center for Advancing Translational Sciences; National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; National Institute on Aging; National Institutes of Health; Advanced Research Projects Agency; National Heart, Lung, and Blood Institute; Weill Cornell Medical College; Stanford Bio-X; School of Medicine, Stanford University","keywords":"Center (category theory); Research center; Artificial intelligence; Medicine; Medical physics; Computer science; Pathology","score_opus":0.6331985731558333,"score_gpt":0.6093027532821427,"score_spread":0.023895819873690605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403901714","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3957842,0.005323186,0.02464147,0.53477466,0.0064292434,0.008328858,0.00009652793,0.00080202124,0.023819812],"genre_scores_gemma":[0.9931523,0.0005605021,0.0013706777,0.0020472205,0.001585058,0.0002384087,0.00007657291,0.000048482834,0.0009207284],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99649423,0.00001855828,0.0014312497,0.00067108154,0.0004908218,0.00089402706],"domain_scores_gemma":[0.9978346,0.0009974312,0.000097541255,0.00013578213,0.00060329406,0.00033134368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038443485,0.00018566361,0.00045659675,0.0007420001,0.00019255374,0.00018526042,0.00016581698,0.0001639632,0.0000991668],"category_scores_gemma":[0.0026900414,0.00015819259,0.000086977976,0.0014351588,0.00025049713,0.00041608146,0.000058093843,0.0007124804,0.00038316078],"study_design_candidate":"design_other","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":[0.00034776583,0.00021441608,0.0044460567,0.0031629119,0.00001526762,0.000008704454,0.0071082255,1.591245e-7,0.000011179601,0.055379905,0.012800065,0.91650534],"study_design_scores_gemma":[0.00018016435,0.0061265877,0.0018973418,0.015052975,0.000019684057,0.00018952916,0.04957245,0.007837208,0.001049317,0.65234196,0.2651452,0.0005875893],"about_ca_topic_score_codex":0.00024637958,"about_ca_topic_score_gemma":0.00007810353,"teacher_disagreement_score":0.91591775,"about_ca_system_score_codex":0.0009034204,"about_ca_system_score_gemma":0.001752793,"threshold_uncertainty_score":0.6450907},"labels":[],"label_agreement":null},{"id":"W4404769350","doi":"10.1016/j.mcpdig.2024.11.004","title":"Gait Speed and Task Specificity in Predicting Lower-Limb Kinematics: A Deep Learning Approach Using Inertial Sensors","year":2024,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings Digital Health","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Fonds de Recherche du Québec - Santé; Institut TransMedTech; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec; Canada First Research Excellence Fund; McGill University","keywords":"Kinematics; Gait; Task (project management); Physical medicine and rehabilitation; Artificial intelligence; Computer science; Lower limb; Inertial measurement unit; Inertial frame of reference; Simulation; Engineering; Medicine; Physics","score_opus":0.05128926919047705,"score_gpt":0.37553682089190993,"score_spread":0.3242475517014329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404769350","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9756809,0.00073827675,0.0005625054,0.00038934965,0.00092120684,0.001339555,0.00003216175,0.00046777012,0.019868277],"genre_scores_gemma":[0.9955872,0.00035018096,0.0010949082,0.00028061302,0.0010686847,0.00003057277,0.000055373224,0.00008854663,0.00144389],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957293,0.0001438402,0.0017437917,0.00085851416,0.00042772305,0.0010968095],"domain_scores_gemma":[0.9982866,0.00052504794,0.0005661745,0.00013724306,0.00013524148,0.00034967545],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0029772094,0.00036452265,0.00074091647,0.00035409842,0.00057176227,0.00023706967,0.00018086212,0.00035989835,0.00002079475],"category_scores_gemma":[0.0008788076,0.0003456468,0.0001323901,0.0007064557,0.00012521308,0.0008812828,0.0002513811,0.0020293638,0.00011918681],"study_design_candidate":"observational","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":[0.001660027,0.0034551623,0.7915298,0.052178394,0.00027986793,0.00009201759,0.055519126,0.000098798046,0.001971328,0.0037943237,0.003919118,0.085502066],"study_design_scores_gemma":[0.00435691,0.0017692624,0.1661959,0.011397671,0.00009504466,0.00012526262,0.041601803,0.7598197,0.0000026400826,0.0037808565,0.009489433,0.0013655443],"about_ca_topic_score_codex":0.00014331366,"about_ca_topic_score_gemma":0.00002277766,"teacher_disagreement_score":0.75972086,"about_ca_system_score_codex":0.00046198256,"about_ca_system_score_gemma":0.000338978,"threshold_uncertainty_score":0.99989957},"labels":[],"label_agreement":null},{"id":"W4409039996","doi":"10.1016/j.mcpdig.2025.100217","title":"Quantifying the Unknowns of Plaque Morphology: The Role of Topological Uncertainty in Coronary Artery Disease","year":2025,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings Digital Health","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Daiichi Sankyo Europe; Duke Clinical Research Institute; St. Jude Medical; Pfizer; Moderna; Belvoir Media Group; MyoKardia; Novo Nordisk; Medicines Company; AstraZeneca; Amarin Corporation; Ironwood Pharmaceuticals, Incorporated; Assistance publique-Hôpitaux de Paris; National Institutes of Health; Regeneron Pharmaceuticals; Boston Scientific Corporation; Idorsia Pharmaceuticals; Brigham and Women's Hospital; Bristol-Myers Squibb; Cleveland Clinic; Alnylam Pharmaceuticals; HLS Therapeutics; GlaxoSmithKline; CSL Behring; Eli Lilly and Company; Amgen; Sanofi; American Heart Association","keywords":"Coronary artery disease; Morphology (biology); Cardiology; Vulnerable plaque; Internal medicine; Medicine; Topology (electrical circuits); Artificial intelligence; Mathematics; Computer science; Biology; Combinatorics; Genetics","score_opus":0.0306922339347514,"score_gpt":0.3303892542005199,"score_spread":0.2996970202657685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409039996","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9877753,0.0014555367,0.000005809183,0.005906267,0.00010965088,0.0008018128,0.00006727915,0.00003369004,0.00384462],"genre_scores_gemma":[0.99758816,0.00031538628,0.000006411789,0.0017662111,0.000044584747,0.000046169676,0.000028535813,0.000010277303,0.00019426999],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983571,0.00003516094,0.00073171366,0.00030498882,0.00024627577,0.0003247459],"domain_scores_gemma":[0.99891627,0.00034148135,0.00022196892,0.00025722588,0.00011448285,0.00014857379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000572376,0.0001612371,0.0004951672,0.00009902059,0.0000911293,0.000022420156,0.00024934372,0.00007037938,0.000055185923],"category_scores_gemma":[0.00045754382,0.0000911236,0.00027384152,0.00036389867,0.0005006657,0.00010911959,0.00014040028,0.00030131164,0.000009526964],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059648504,0.0006656725,0.9809796,0.0004950907,0.00007620559,0.00000415409,0.00037455358,0.000003534262,0.00004564685,0.0057621505,0.000592909,0.010403961],"study_design_scores_gemma":[0.0012274372,0.0004550359,0.9843002,0.0005236622,0.00006918629,0.000031893058,0.0066870768,0.00020247567,0.00005989485,0.0045038937,0.0018468376,0.00009238541],"about_ca_topic_score_codex":0.00006290244,"about_ca_topic_score_gemma":0.000018057624,"teacher_disagreement_score":0.010311576,"about_ca_system_score_codex":0.00007278093,"about_ca_system_score_gemma":0.0005683962,"threshold_uncertainty_score":0.37159124},"labels":[],"label_agreement":null},{"id":"W4411140055","doi":"10.1016/j.mcpdig.2025.100237","title":"Medication Adherence Technologies: A Classification Taxonomy Based on Features","year":2025,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings Digital Health","topic":"Medication Adherence and Compliance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Research Institute for Aging; National Research Council Canada; University of Toronto; Canadian Patient Safety Institute; University of Waterloo; Canadian Pharmacists Association; McMaster University","funders":"National Research Council Canada; Canadian Institutes of Health Research; Government of Canada; National Research Council","keywords":"Taxonomy (biology); Computer science; Medicine; Information retrieval; Computational biology; Biology; Zoology","score_opus":0.0968047584634787,"score_gpt":0.3820015108538147,"score_spread":0.285196752390336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411140055","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016241832,0.0010029,0.010319442,0.19443466,0.0010859688,0.0048833718,0.000042204985,0.0023673149,0.7696223],"genre_scores_gemma":[0.9725731,0.0001685142,0.0011887087,0.010421472,0.000060908267,0.00068673317,0.000075023774,0.000013760623,0.014811779],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99800795,0.00000796164,0.0006401153,0.00058015075,0.00041767673,0.00034615753],"domain_scores_gemma":[0.9986654,0.00013706657,0.00041787422,0.00036192394,0.0002458807,0.00017186214],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00045764432,0.00019981402,0.0003706868,0.00028775528,0.00014552058,0.00008411486,0.00031151465,0.00019907879,0.00015686246],"category_scores_gemma":[0.0013850431,0.00017133538,0.00008522193,0.0007737125,0.00020516597,0.00019987824,0.00005677984,0.0004909901,0.0008831929],"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":[0.0002831479,0.00055718416,0.02626294,0.0007974283,0.000022574663,0.0000011425557,0.000044615295,1.1786023e-7,0.00002930123,0.0076600173,0.33067,0.6336715],"study_design_scores_gemma":[0.005390352,0.004037789,0.20341226,0.0071102004,0.00006660846,0.000026347945,0.0062403292,0.0056466623,0.00061213644,0.01108407,0.75574535,0.0006279092],"about_ca_topic_score_codex":0.0000071775567,"about_ca_topic_score_gemma":6.5744115e-7,"teacher_disagreement_score":0.95633125,"about_ca_system_score_codex":0.0002585639,"about_ca_system_score_gemma":0.0011437922,"threshold_uncertainty_score":0.99989474},"labels":[],"label_agreement":null},{"id":"W4412830063","doi":"10.1016/j.mcpdig.2025.100252","title":"An Automated Mobile Cognitive Test for the Identification of Cognitive Impairment: A Cross-sectional Feasibility and Diagnostic Study","year":2025,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings Digital Health","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National University of Singapore","keywords":"Identification (biology); Cognition; Cognitive impairment; Test (biology); Cross-sectional study; Computer science; Medicine; Psychiatry; Pathology","score_opus":0.04610961812761663,"score_gpt":0.4625558946090571,"score_spread":0.4164462764814405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412830063","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9879805,0.0002881696,0.0002743209,0.00016317914,0.000099045166,0.009997239,0.0006039064,0.00016267088,0.00043099164],"genre_scores_gemma":[0.9972044,0.000087727,0.000008393165,0.00029282365,0.000054690106,0.0016040814,0.00016593763,0.000018866709,0.00056307635],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974501,0.00003158794,0.0010342853,0.0006431274,0.00044163314,0.000399282],"domain_scores_gemma":[0.99293196,0.0046619205,0.0003967339,0.00012500622,0.0016798489,0.00020455652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023546133,0.00019822326,0.00038689305,0.00018092403,0.0003876921,0.00029932024,0.00012627033,0.00007493514,0.000031820826],"category_scores_gemma":[0.007640239,0.00015151036,0.00010651176,0.0004900639,0.00043597637,0.00044259042,0.00009763062,0.00022650238,0.000011065676],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012648007,0.0074278107,0.9776928,0.00075243047,0.00016762425,0.0000011910937,0.00079232943,6.636199e-8,0.000019644578,0.000016717058,0.00012544796,0.011739137],"study_design_scores_gemma":[0.0063465787,0.012153346,0.96876913,0.00040696305,0.00014328107,0.00001063061,0.010257007,0.0012285641,0.0002174923,0.0003453412,0.000015609729,0.000106078835],"about_ca_topic_score_codex":0.00003599265,"about_ca_topic_score_gemma":0.000006768316,"teacher_disagreement_score":0.011633057,"about_ca_system_score_codex":0.0001520994,"about_ca_system_score_gemma":0.00048423343,"threshold_uncertainty_score":0.91466314},"labels":[],"label_agreement":null},{"id":"W4413450113","doi":"10.1016/j.mcpdig.2025.100257","title":"A Standardized Temporal Segmentation Framework and Annotation Resource Library in Robotic Surgery","year":2025,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings Digital Health","topic":"Surgical Simulation and Training","field":"Medicine","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":"Women's Health Research Institute","funders":"Intuitive Surgical; AstraZeneca","keywords":"Annotation; Segmentation; Computer science; Resource (disambiguation); Robotic surgery; Artificial intelligence; Information retrieval; Computer vision","score_opus":0.037696220429952555,"score_gpt":0.3581539543973012,"score_spread":0.3204577339673486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413450113","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96443325,0.00047479698,0.00067626056,0.011734597,0.0001490436,0.00070460886,0.000010470449,0.0002411308,0.021575827],"genre_scores_gemma":[0.9936599,0.00007928402,0.0010944234,0.004094077,0.000060688955,0.000016498316,0.00010330165,0.000017861788,0.00087396323],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985268,0.00001710074,0.00067171024,0.00031195857,0.00021286473,0.0002595796],"domain_scores_gemma":[0.9988396,0.00072137156,0.00017485385,0.00005968575,0.000033418404,0.00017103738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055222225,0.00012952324,0.00043751945,0.00029143417,0.000076169184,0.0001511562,0.00003271526,0.000109941684,0.000044660766],"category_scores_gemma":[0.0007350186,0.000121027675,0.000067017514,0.00070742116,0.00005928809,0.00051743427,0.00003549226,0.00027744603,0.000007950984],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010473949,0.00015102673,0.79301924,0.00051998283,0.000022276849,0.000008346484,0.0007252683,0.0000034906072,7.221814e-7,0.0012921,0.001925991,0.20128414],"study_design_scores_gemma":[0.011368967,0.00081328553,0.8678895,0.006376943,0.000048890237,0.000027893508,0.007613717,0.0036419774,0.00004752997,0.024493463,0.0771571,0.0005207136],"about_ca_topic_score_codex":0.000005824384,"about_ca_topic_score_gemma":6.856125e-7,"teacher_disagreement_score":0.20076342,"about_ca_system_score_codex":0.00008529844,"about_ca_system_score_gemma":0.0003029739,"threshold_uncertainty_score":0.49353653},"labels":[],"label_agreement":null},{"id":"W4413950915","doi":"10.1016/j.mcpdig.2025.100260","title":"A Technology Selection Tool Applying Multiple Criteria Decision Analysis for Virtual Care Implementation","year":2025,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings Digital Health","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan; Royal University Hospital; Saskatchewan Health Authority","funders":"Mitacs; University of Saskatchewan","keywords":"Selection (genetic algorithm); Computer science; Decision analysis; Management science; Process management; Operations research; Engineering; Artificial intelligence; Mathematics; Statistics","score_opus":0.03462402537142364,"score_gpt":0.4383525939174199,"score_spread":0.40372856854599626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413950915","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9252048,0.0002067192,0.06470411,0.0043331305,0.00038127005,0.004191367,0.00016496894,0.00038285137,0.0004307786],"genre_scores_gemma":[0.9885305,0.000055925353,0.0067259017,0.0025836586,0.00017669426,0.0008591786,0.0007981696,0.000027054772,0.00024292768],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9972302,0.0000075844164,0.0012670781,0.0006063934,0.0003023698,0.0005863798],"domain_scores_gemma":[0.9984003,0.00023719801,0.0004249777,0.00013587804,0.00064435566,0.0001572923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005582496,0.00023024029,0.00065131014,0.0013770716,0.00036303204,0.00010106307,0.00010763127,0.00015607779,0.00008643823],"category_scores_gemma":[0.0006710052,0.0002240563,0.0001898503,0.0024680134,0.00004704239,0.0003733135,0.000063639156,0.00022326628,0.000012373112],"study_design_candidate":"observational","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":[0.00047782678,0.0000867976,0.42767078,0.00057956896,0.000173558,3.812136e-7,0.0005062715,4.2865665e-7,0.000071266695,0.0004708716,0.0061830794,0.5637792],"study_design_scores_gemma":[0.03129042,0.016249971,0.56451064,0.0010676734,0.002039266,0.00005721181,0.112225406,0.0050579333,0.0025214704,0.0054314896,0.25857162,0.0009768774],"about_ca_topic_score_codex":0.00008341917,"about_ca_topic_score_gemma":0.00014064868,"teacher_disagreement_score":0.56280226,"about_ca_system_score_codex":0.0005705196,"about_ca_system_score_gemma":0.00059541746,"threshold_uncertainty_score":0.91367507},"labels":[],"label_agreement":null},{"id":"W4415641472","doi":"10.1016/j.mcpdig.2025.100301","title":"A Scoping Review of Large Language Models in Personal Sleep Wellness","year":2025,"lang":"en","type":"review","venue":"Mayo Clinic Proceedings Digital Health","topic":"Sleep and related disorders","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"University of Manitoba","keywords":"Personalization; Wearable computer; Sleep (system call); Wearable technology; Everyday life; Vocabulary; Cognition","score_opus":0.05238882901346747,"score_gpt":0.43785923366054746,"score_spread":0.38547040464708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415641472","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000094987245,0.90631866,0.000021257676,0.00019514971,0.0007369381,0.0034043624,0.00025918352,0.00009890419,0.08895603],"genre_scores_gemma":[0.000095379495,0.99498105,0.000013491538,0.0013458143,0.00009629994,0.00039563427,0.00024029819,0.00008376389,0.002748274],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9947991,0.00009351321,0.0027436588,0.0010085634,0.00038573612,0.0009694573],"domain_scores_gemma":[0.99738514,0.00048833905,0.001504789,0.0003027973,0.00010152246,0.00021743149],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001694113,0.0006272047,0.0037674196,0.0006067515,0.00007851708,0.000050669238,0.00061618164,0.0007123467,0.0005827932],"category_scores_gemma":[0.00039718804,0.00054116483,0.0008566712,0.001499857,0.00010274661,0.00028122473,0.00022328437,0.0012198938,0.00028603402],"study_design_candidate":"systematic_review","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":[0.000011990615,0.00035363066,0.0000098898845,0.45630664,0.00014475951,0.0000068666222,0.00090115453,1.1878847e-8,2.2976583e-10,0.00068038073,0.0026440406,0.5389406],"study_design_scores_gemma":[0.00092703983,0.00015961372,9.899626e-7,0.89533794,0.0002881974,0.000028061677,0.0013501756,0.000008367405,8.589103e-9,0.00015789723,0.10129751,0.0004441748],"about_ca_topic_score_codex":0.00005274296,"about_ca_topic_score_gemma":0.0000105495255,"teacher_disagreement_score":0.53849643,"about_ca_system_score_codex":0.00020112966,"about_ca_system_score_gemma":0.001248586,"threshold_uncertainty_score":0.999704},"labels":[],"label_agreement":null}]}