{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":35,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":35,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"328dc2475acf","filters":{"venue":"European Heart Journal - Digital Health"}},"results":[{"id":"W3127649143","doi":"10.1093/ehjdh/ztab005","title":"Barriers and facilitators of the uptake of digital health technology in cardiovascular care: a systematic scoping review","year":2021,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":260,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Population Health Research Institute; McMaster University; Impact","funders":"Canadian Institutes of Health Research","keywords":"CINAHL; Thematic analysis; Medicine; Workload; MEDLINE; Health care; Qualitative research; Family medicine; Nursing; Psychological intervention","authors":[{"name":"Sera Whitelaw","is_ca":true},{"name":"Danielle Pellegrini","is_ca":true},{"name":"Mamas A. Mamas","is_ca":false},{"name":"Martín Cowie","is_ca":false},{"name":"Harriette G.C. Van Spall","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04852115125765442,"gpt":0.3929353654732305,"spread":0.3444142142155761,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03378547,0.001412809,0.006129731,0.01462035,0.001457398,0.004980897,0.002259851,0.002522898,0.003037838],"category_scores_gemma":[0.1448527,0.001754502,0.007008858,0.01566916,0.001648855,0.005102834,0.002923608,0.001879177,0.0002892417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006960667,"about_ca_system_score_gemma":0.03072253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01280808,"about_ca_topic_score_gemma":0.03229961,"domain_scores_codex":[0.971298,0.01189154,0.009675518,0.001296897,0.005143718,0.0006942039],"domain_scores_gemma":[0.8576214,0.1135357,0.01576158,0.001355776,0.01090253,0.0008230114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00009576163,0.00003559427,0.002533662,0.9353254,0.004520882,0.0001189534,0.002283446,0.0001302661,0.0001050947,0.0005483255,0.001238936,0.05306369],"study_design_scores_gemma":[0.00004497313,0.00006920246,0.003020123,0.9768584,0.01164324,0.0001222521,0.001442348,0.00007704581,0.00009499962,0.0001791181,0.006425584,0.00002275761],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.005884046,0.9901726,0.0005235535,0.000832445,0.0001542533,0.001260605,0.0003350325,0.00001114071,0.0008262146],"genre_scores_gemma":[0.04294377,0.9517185,0.00166084,0.0005985132,0.00007018782,0.002537296,0.0002777396,0.000009206543,0.0001838864],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.03378547,"threshold_uncertainty_score":0.1786768,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4280518374","doi":"10.1093/ehjdh/ztac025","title":"Applications of artificial intelligence and machine learning in heart failure","year":2022,"lang":"en","type":"review","venue":"European Heart Journal - Digital Health","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":97,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Impact; Population Health Research Institute; McMaster University","funders":"","keywords":"Medicine; Artificial intelligence; Machine learning; Raw data; Cardiac decompensation; Decompensation; Health care; Heart failure; Computer science","authors":[{"name":"Tauben Averbuch","is_ca":true},{"name":"Kristen Sullivan","is_ca":true},{"name":"Andrew J. Sauer","is_ca":false},{"name":"Mamas Mamas","is_ca":false},{"name":"Adriaan A. Voors","is_ca":false},{"name":"Chris P Gale","is_ca":false},{"name":"Marco Metra","is_ca":false},{"name":"Neal G. Ravindra","is_ca":false},{"name":"Harriette G.C. Van Spall","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3049971717223502,"gpt":0.462322124839359,"spread":0.1573249531170087,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001936615,0.0008917404,0.001655128,0.003277819,0.0003190919,0.001466899,0.0007546501,0.00202597,0.003232928],"category_scores_gemma":[0.003561099,0.000333763,0.001104934,0.002895846,0.001169272,0.001899629,0.00111213,0.003276703,0.001222279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001045541,"about_ca_system_score_gemma":0.001726849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001362676,"about_ca_topic_score_gemma":0.001641693,"domain_scores_codex":[0.9989859,0.0003652814,0.0001478472,0.000110686,0.000338845,0.00005149994],"domain_scores_gemma":[0.9967808,0.002658538,0.0001454317,0.00006708643,0.0002941692,0.00005394198],"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":[0.00004492622,0.0000699651,0.0002645898,0.02937428,0.0002420323,0.0001469633,0.0001315134,0.001069741,0.0004960043,0.02301072,0.01461217,0.9305371],"study_design_scores_gemma":[0.00002845348,0.0001593958,0.002525228,0.0265307,0.0002338058,0.001340851,0.0001700407,0.000634075,0.0005487904,0.02731667,0.9404506,0.00006138882],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00008634243,0.9974463,0.0003830496,0.0006875899,0.0002116154,0.000006982071,0.000008954151,0.0000065947,0.001162623],"genre_scores_gemma":[0.00121403,0.9974117,0.0004847196,0.0003770709,0.0002482696,0.00001168937,0.00001362239,0.000002347592,0.0002366916],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003277819,"threshold_uncertainty_score":0.0108152,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3195815158","doi":"10.1093/ehjdh/ztab048","title":"Deep learning analysis of resting electrocardiograms for the detection of myocardial dysfunction, hypertrophy, and ischaemia: a systematic review","year":2021,"lang":"en","type":"review","venue":"European Heart Journal - Digital Health","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Jewish General Hospital","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Cardiology; Internal medicine; Left ventricular hypertrophy; Myocardial ischaemia; Ischemia; Myocardial ischemia; Electrocardiography; Blood pressure","authors":[{"name":"Ghalib Al Hinai","is_ca":true},{"name":"Samer Jammoul","is_ca":true},{"name":"Zara Vajihi","is_ca":true},{"name":"Jonathan Afilalo","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04898089170747566,"gpt":0.3461857832591219,"spread":0.2972048915516463,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004190647,0.001189141,0.00588708,0.00568931,0.0002978182,0.001904938,0.001550219,0.001627189,0.00351738],"category_scores_gemma":[0.02761264,0.0006510203,0.005752881,0.004801311,0.0005399035,0.001631123,0.0008658734,0.0009022072,0.0004085557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001721437,"about_ca_system_score_gemma":0.004782123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004809924,"about_ca_topic_score_gemma":0.01220473,"domain_scores_codex":[0.9971897,0.0007250838,0.001135169,0.0003052392,0.0005538571,0.00009102333],"domain_scores_gemma":[0.9748337,0.02067853,0.002497653,0.000233249,0.001628279,0.0001284894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0002926928,0.0000330369,0.001216242,0.8784539,0.007630097,0.0001062633,0.0000980832,0.000219939,0.0001836344,0.0002388142,0.002278959,0.1092483],"study_design_scores_gemma":[0.0002587097,0.0003226266,0.007032604,0.9041505,0.06231783,0.0006623828,0.0001647922,0.0003113397,0.0002690215,0.0005326564,0.02391485,0.00006267132],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005194269,0.9988562,0.000116145,0.0001350776,0.00005891846,0.00005430463,0.0001280837,0.000004972508,0.000126931],"genre_scores_gemma":[0.008540249,0.9899848,0.0004723051,0.0004856118,0.0001044307,0.0001573113,0.0001648651,0.000004179637,0.00008638208],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00588708,"threshold_uncertainty_score":0.02216256,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3214762646","doi":"10.1093/ehjdh/ztab101","title":"Development of a machine learning model using electrocardiogram signals to improve acute pulmonary embolism screening","year":2021,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Impact; McMaster University; Population Health Research Institute","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; National Institutes of Health; National Center for Advancing Translational Sciences; Icahn School of Medicine at Mount Sinai","keywords":"Medicine; Pulmonary embolism; Receiver operating characteristic; Artificial intelligence; Retrospective cohort study; Cohort; Internal medicine; Electrocardiography; Cardiology; Machine learning","authors":[{"name":"Sulaiman Somani","is_ca":false},{"name":"Hossein Honarvar","is_ca":false},{"name":"Sukrit Narula","is_ca":true},{"name":"Isotta Landi","is_ca":false},{"name":"Shawn Lee","is_ca":false},{"name":"Yeraz Khachatoorian","is_ca":false},{"name":"Arsalan Rehmani","is_ca":false},{"name":"Andrew Kim","is_ca":false},{"name":"Jessica K. De Freitas","is_ca":false},{"name":"Shelly Teng","is_ca":false},{"name":"Suraj K. Jaladanki","is_ca":false},{"name":"Arvind Kumar","is_ca":false},{"name":"Adam Russak","is_ca":false},{"name":"Shan Zhao","is_ca":false},{"name":"Robert Freeman","is_ca":false},{"name":"Matthew A. Levin","is_ca":false},{"name":"Girish N. Nadkarni","is_ca":false},{"name":"Alexander C. Kagen","is_ca":false},{"name":"Edgar Argulian","is_ca":false},{"name":"Benjamin S. Glicksberg","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05031416966398888,"gpt":0.3297902879970327,"spread":0.2794761183330439,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00164562,0.0007729473,0.000618609,0.0006784016,0.0002326673,0.0007603799,0.0007900918,0.0009436547,0.001156035],"category_scores_gemma":[0.005340439,0.0003134628,0.0006445868,0.0003744537,0.0001987114,0.000670639,0.0005058773,0.001123612,0.0005845042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006542263,"about_ca_system_score_gemma":0.00113357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007541261,"about_ca_topic_score_gemma":0.004949122,"domain_scores_codex":[0.9996082,0.0001225879,0.00003565521,0.0001163957,0.00007208288,0.00004511528],"domain_scores_gemma":[0.998359,0.0009312851,0.0001274604,0.00007395205,0.0004534863,0.00005480701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002340819,0.0003606477,0.02571375,0.00005626363,0.0002319053,0.0001333113,0.00006072501,0.8067868,0.004496161,0.001029649,0.001954987,0.1589417],"study_design_scores_gemma":[0.000004410314,0.00002817442,0.0005816444,0.000004498999,0.00001088016,0.00001162568,0.000002565543,0.9985471,0.0004653383,0.000256042,0.00008489798,0.000002702703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2669723,0.0006643368,0.7249406,0.001582677,0.0001495703,0.000152047,0.0006412026,0.002703382,0.002193773],"genre_scores_gemma":[0.9090309,0.0001874461,0.08811957,0.0002749714,0.00004914856,0.0001456974,0.0007901917,0.00004109077,0.001360984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007541261,"threshold_uncertainty_score":0.01499474,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4409323950","doi":"10.1093/ehjdh/ztaf034","title":"Development and multinational validation of an ensemble deep learning algorithm for detecting and predicting structural heart disease using noisy single-lead electrocardiograms","year":2025,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Institutes of Health; Yale University; National Heart, Lung, and Blood Institute; Bristol-Myers Squibb Canada; National Institute on Aging; Bristol-Myers Squibb; Doris Duke Charitable Foundation","keywords":"Computer science; Multinational corporation; Artificial intelligence; Ensemble learning; Lead (geology); Algorithm; Cross-validation; Machine learning; Pattern recognition (psychology); Finance","authors":[{"name":"Arya Aminorroaya","is_ca":false},{"name":"Lovedeep Singh Dhingra","is_ca":false},{"name":"Aline F Pedroso","is_ca":false},{"name":"Sumukh Vasisht Shankar","is_ca":false},{"name":"Andreas Coppi","is_ca":false},{"name":"Akshay Khunte","is_ca":false},{"name":"Murilo Foppa","is_ca":false},{"name":"Luisa C Brant","is_ca":false},{"name":"Sandhi Maria Barreto","is_ca":false},{"name":"Antônio Luiz Pinho Ribeiro","is_ca":false},{"name":"Harlan M. Krumholz","is_ca":false},{"name":"Evangelos K. Oikonomou","is_ca":false},{"name":"Rohan Khera","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03511543079916488,"gpt":0.3273124225099162,"spread":0.2921969917107513,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005788517,0.001024937,0.0006056334,0.0008191644,0.0003230275,0.0004783369,0.001089903,0.0009164062,0.000823778],"category_scores_gemma":[0.006943753,0.0002677594,0.0005677766,0.0004387345,0.0003161387,0.0005171901,0.001048096,0.0009151812,0.000308774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000623072,"about_ca_system_score_gemma":0.0009065178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007466775,"about_ca_topic_score_gemma":0.006426233,"domain_scores_codex":[0.9990212,0.0004257819,0.00007840253,0.0002468482,0.0001461264,0.000081759],"domain_scores_gemma":[0.9970375,0.001315,0.0001911414,0.0003700909,0.0009403106,0.0001459701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008443301,0.001445545,0.1506829,0.0000773112,0.0007015728,0.0001869313,0.0001207381,0.521156,0.009844348,0.0005858823,0.004808112,0.3095464],"study_design_scores_gemma":[0.00005332828,0.0002242906,0.007963301,0.00000905318,0.00003953199,0.00003339933,0.00001968391,0.9886748,0.002532935,0.0002038397,0.0002371363,0.000008784263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9299641,0.0003597728,0.06631222,0.000272638,0.0001031875,0.0001931594,0.0006565093,0.0008551748,0.001283184],"genre_scores_gemma":[0.9580749,0.00007668217,0.03898166,0.0001359669,0.00002094851,0.000138366,0.00155272,0.00002946698,0.0009893286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007466775,"threshold_uncertainty_score":0.03061301,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4224250841","doi":"10.1093/ehjdh/ztac004","title":"Harnessing feature extraction capacities from a pre-trained convolutional neural network (VGG-16) for the unsupervised distinction of aortic outflow velocity profiles in patients with severe aortic stenosis","year":2022,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Technische Universität München","keywords":"Cardiology; Internal medicine; Medicine; Stenosis; Aortic valve stenosis; Aortic valve; Hazard ratio; Confidence interval","authors":[{"name":"Mark Lachmann","is_ca":false},{"name":"Elena Rippen","is_ca":false},{"name":"Daniel Rueckert","is_ca":false},{"name":"Tibor Schuster","is_ca":true},{"name":"Erion Xhepa","is_ca":false},{"name":"Moritz von Scheidt","is_ca":false},{"name":"Costanza Pellegrini","is_ca":false},{"name":"Teresa Trenkwalder","is_ca":false},{"name":"Tobias Rheude","is_ca":false},{"name":"Anja Stundl","is_ca":false},{"name":"R Thalmann","is_ca":false},{"name":"Gerhard Harmsen","is_ca":false},{"name":"Shinsuke Yuasa","is_ca":false},{"name":"Heribert Schunkert","is_ca":false},{"name":"Adnan Kastrati","is_ca":false},{"name":"Michael Joner","is_ca":false},{"name":"Christian Kupatt","is_ca":false},{"name":"Karl‐Ludwig Laugwitz","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02370293776615857,"gpt":0.3027257709217228,"spread":0.2790228331555642,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005084574,0.000655294,0.0004136571,0.000553854,0.0001371553,0.0004153956,0.0003529351,0.0003641041,0.0005382769],"category_scores_gemma":[0.001098942,0.0002127867,0.0005113322,0.0003194627,0.0002016386,0.0002661937,0.0004878597,0.0004003663,0.0002141171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003892797,"about_ca_system_score_gemma":0.0006314297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006886128,"about_ca_topic_score_gemma":0.008003846,"domain_scores_codex":[0.9998453,0.00002854944,0.00001092308,0.0000525064,0.00002059487,0.00004207259],"domain_scores_gemma":[0.9997713,0.0001050801,0.00003049566,0.0000280594,0.00003990697,0.00002511391],"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":[0.001248038,0.0005435992,0.1708897,0.0001860519,0.0004264808,0.0008164937,0.0003187382,0.1513375,0.1074346,0.0007674916,0.003807188,0.5622241],"study_design_scores_gemma":[0.00003202172,0.000303169,0.07484708,0.00002919188,0.0001289967,0.0003337344,0.00006749697,0.900557,0.02196442,0.0007992851,0.0009106802,0.00002691284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9170032,0.000480483,0.07978441,0.000248367,0.00004638953,0.00006179691,0.0007087513,0.0007025654,0.0009642005],"genre_scores_gemma":[0.982843,0.00011048,0.01564284,0.00004637541,0.00001313484,0.00003185256,0.0007797566,0.00001886367,0.000513624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006886128,"threshold_uncertainty_score":0.01369208,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388667507","doi":"10.1093/ehjdh/ztad071","title":"Feasibility and accuracy of real-time 3D-holographic graft length measurements","year":2023,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"St. Paul's Hospital; University of British Columbia","funders":"University of Galway","keywords":"Medicine; Artery; Nuclear medicine; Holography; Radiology; Surgery","authors":[{"name":"Tsung-Ying Tsai","is_ca":false},{"name":"Shigetaka Kageyama","is_ca":false},{"name":"XingQiang He","is_ca":false},{"name":"Giulio Pompilio","is_ca":false},{"name":"Daniele Andreini","is_ca":false},{"name":"Gianluca Pontone","is_ca":false},{"name":"Mark La Meir","is_ca":false},{"name":"Johan De Mey","is_ca":false},{"name":"Kaoru Tanaka","is_ca":false},{"name":"Torsten Doenst","is_ca":false},{"name":"John D. Puskas","is_ca":false},{"name":"Ulf Teichgräber","is_ca":false},{"name":"Ulrich Schneider","is_ca":false},{"name":"Himanshu Gupta","is_ca":false},{"name":"Jonathon Leipsic","is_ca":true},{"name":"Scot Garg","is_ca":false},{"name":"Pruthvi C. Revaiah","is_ca":false},{"name":"Maciej Stanuch","is_ca":false},{"name":"Andrzej Skalski","is_ca":false},{"name":"Yoshinobu Onuma","is_ca":false},{"name":"Patrick W. Serruys","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2012091056447465,"gpt":0.3971525682336164,"spread":0.1959434625888699,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01673019,0.0004308199,0.0004679621,0.001365475,0.0002956889,0.001506756,0.0008298201,0.0006996001,0.001115606],"category_scores_gemma":[0.03215472,0.0005078805,0.0004945919,0.0005645566,0.0007820409,0.0008706474,0.000915917,0.0004143839,0.0005713206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003375016,"about_ca_system_score_gemma":0.00045887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001029028,"about_ca_topic_score_gemma":0.001243216,"domain_scores_codex":[0.9827257,0.009591189,0.001581315,0.002070816,0.003585548,0.0004454463],"domain_scores_gemma":[0.9639652,0.02134127,0.00415044,0.004063214,0.00608287,0.0003971011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006406124,0.0003028621,0.7367247,0.0004181726,0.0003155194,0.0004392505,0.001662477,0.004226639,0.07654287,0.0005335591,0.000793089,0.1716347],"study_design_scores_gemma":[0.0002921272,0.002427613,0.8822063,0.0001314551,0.0004221296,0.002774159,0.0009311536,0.0473404,0.06014827,0.0005202416,0.002656868,0.0001492121],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9408521,0.00166656,0.05410981,0.0001217962,0.0001040206,0.0001349345,0.0002959993,0.0002525009,0.002462286],"genre_scores_gemma":[0.9795278,0.0001693875,0.01971032,0.00002683847,0.00003656773,0.00007147082,0.0001270463,0.00004124403,0.0002893097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01673019,"threshold_uncertainty_score":0.08847874,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3170186245","doi":"10.1093/ehjdh/ztab055","title":"Voice-based screening for SARS-CoV-2 exposure in cardiovascular clinics","year":2021,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"Montreal Heart Institute; University of Ottawa; McGill University; Université de Montréal; McGill University Health Centre","funders":"McGill University Health Centre; Amazon Web Services","keywords":"Medicine; Context (archaeology); Cohen's kappa; Inter-rater reliability; Health care; Confidence interval; Kappa; Family medicine; Rating scale; Internal medicine; Statistics","authors":[{"name":"Abhinav Sharma","is_ca":true},{"name":"Emily Oulousian","is_ca":true},{"name":"Jiayi Ni","is_ca":true},{"name":"Renato D. Lópes","is_ca":false},{"name":"Matthew P. Cheng","is_ca":true},{"name":"Julie Label","is_ca":true},{"name":"Filipe Henriques","is_ca":true},{"name":"Claudia Lighter","is_ca":true},{"name":"Nadia Giannetti","is_ca":true},{"name":"Robert Avram","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3217032978222115,"gpt":0.4592702664062936,"spread":0.1375669685840821,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005512107,0.0003046993,0.0003155205,0.0006251512,0.0003146338,0.0007476877,0.0005102849,0.0005278448,0.006096993],"category_scores_gemma":[0.02443638,0.0001666139,0.0003206038,0.0002515218,0.0002833247,0.000483716,0.0009026694,0.0003684772,0.001195761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003071732,"about_ca_system_score_gemma":0.000669974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001129229,"about_ca_topic_score_gemma":0.002149406,"domain_scores_codex":[0.9943895,0.003908213,0.0004672801,0.0004165549,0.0005892446,0.0002292558],"domain_scores_gemma":[0.9818622,0.01000339,0.004376272,0.0006914758,0.002233535,0.000833079],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002379533,0.001177425,0.7179268,0.001286064,0.0001210158,0.0008450217,0.006889523,0.0005488853,0.0139527,0.0002775047,0.007166195,0.2474293],"study_design_scores_gemma":[0.0003826751,0.01210484,0.9456252,0.0007255197,0.0002235981,0.00372069,0.007172405,0.006967311,0.01010976,0.0006020416,0.01223079,0.0001351461],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858719,0.0005732893,0.005144324,0.000765319,0.00007540803,0.0007702365,0.0006774501,0.0001792411,0.005942863],"genre_scores_gemma":[0.9912955,0.0003246648,0.006502497,0.00046338,0.00009760905,0.0003327738,0.0001636236,0.0000105615,0.0008094691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006096993,"threshold_uncertainty_score":0.02915114,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3158681032","doi":"10.1093/ehjdh/ztab044","title":"Remote monitoring of patients with heart failure during the first national lockdown for COVID-19 in France","year":2021,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"COVID-19 Clinical Research Studies","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Agence Nationale de la Recherche","keywords":"Medicine; Coronavirus disease 2019 (COVID-19); Atrial fibrillation; Heart failure; Heart rate; Blood pressure; Internal medicine; Vital signs; Emergency medicine; Cardiology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Anesthesia","authors":[{"name":"Sylvain Ploux","is_ca":false},{"name":"Marc Strik","is_ca":false},{"name":"Saer Abu-Alrub","is_ca":false},{"name":"F. Daniel Ramirez","is_ca":true},{"name":"Samuel Buliard","is_ca":false},{"name":"Hugo Marchand","is_ca":false},{"name":"François Picard","is_ca":false},{"name":"Romain Eschalier","is_ca":false},{"name":"Michel Haı̈ssaguerre","is_ca":false},{"name":"Pierre Bordachar","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06166184558678729,"gpt":0.415034096047281,"spread":0.3533722504604938,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007755192,0.0003145789,0.000450903,0.0006730753,0.000407776,0.0006196889,0.0002921642,0.0004879357,0.001110409],"category_scores_gemma":[0.002291387,0.0001245819,0.0005264542,0.0004116834,0.0002333224,0.000384614,0.0004542271,0.000556268,0.0001651898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001060121,"about_ca_system_score_gemma":0.0006634301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01927737,"about_ca_topic_score_gemma":0.01670504,"domain_scores_codex":[0.9991916,0.000254432,0.00006238949,0.0001886351,0.0001329195,0.0001699552],"domain_scores_gemma":[0.997286,0.0005009742,0.001184689,0.0001137613,0.0002634207,0.000651143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003140543,0.000111475,0.9924367,0.0000213163,0.00005079617,0.0001348873,0.0002211025,0.00008758363,0.0004016205,0.00001073556,0.0001887903,0.006020864],"study_design_scores_gemma":[0.0000145358,0.0003784891,0.9988326,0.00001244315,0.00001323694,0.0001016561,0.0001910379,0.000162206,0.00007459195,0.00000477482,0.0002093843,0.000005038341],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991007,0.000238177,0.000141433,0.00006483414,0.000008644519,0.00001190031,0.0001716068,0.000008348255,0.0002543727],"genre_scores_gemma":[0.9992326,0.00009556317,0.0001301816,0.00008055887,0.00002405639,0.00001570104,0.0002709546,0.00000213939,0.0001481942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01927737,"threshold_uncertainty_score":0.03833032,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4376140131","doi":"10.1093/ehjdh/ztad032","title":"The AppCare-HF randomized clinical trial: a feasibility study of a novel self-care support mobile app for individuals with chronic heart failure","year":2023,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Heart Failure Treatment and Management","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Japan Science and Technology Agency; Center of Innovation Program; Japan Society for the Promotion of Science; Fukuda Foundation for Medical Technology; Pfizer Health Research Foundation; Pfizer; Japan Agency for Medical Research and Development; Health Research Foundation","keywords":"Medicine; Heart failure; Randomized controlled trial; Self care; Physical therapy; Intensive care medicine; Internal medicine; Health care","authors":[{"name":"Takashi Yokota","is_ca":false},{"name":"Arata Fukushima","is_ca":false},{"name":"Miyuki Tsuchihashi‐Makaya","is_ca":false},{"name":"Takahiro Abe","is_ca":false},{"name":"Shingo Takada","is_ca":false},{"name":"Takaaki Furihata","is_ca":false},{"name":"Naoki Ishimori","is_ca":false},{"name":"Takeo Fujino","is_ca":false},{"name":"Shintaro Kinugawa","is_ca":false},{"name":"Masayuki Ohta","is_ca":false},{"name":"Shigeo Kakinoki","is_ca":false},{"name":"Isao Yokota","is_ca":false},{"name":"Akira Endoh","is_ca":false},{"name":"Masanori Yoshino","is_ca":false},{"name":"Hiroyuki Tsutsui","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08915800788675032,"gpt":0.410836304254698,"spread":0.3216782963679477,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00331015,0.001946414,0.002613817,0.0005883591,0.0006713005,0.001197936,0.0009212664,0.002530207,0.005187872],"category_scores_gemma":[0.003926887,0.0009001658,0.001708965,0.0004783912,0.001342146,0.001627847,0.0007993269,0.002125095,0.0007882921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005296047,"about_ca_system_score_gemma":0.001384709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009868225,"about_ca_topic_score_gemma":0.001457525,"domain_scores_codex":[0.9978976,0.00123743,0.0001903748,0.0003028976,0.0001690418,0.0002026815],"domain_scores_gemma":[0.9983755,0.0006222387,0.0003295841,0.0001316358,0.0001456471,0.0003952882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","study_design_scores_codex":[0.9619887,0.02520136,0.001120156,0.0006907597,0.0005757409,0.00004021313,0.00006596227,0.0001183286,0.001250233,0.00008861763,0.0003486226,0.008511237],"study_design_scores_gemma":[0.9153067,0.08197224,0.001822903,0.00002313309,0.0002769995,0.00001013748,0.00001964343,0.0001689322,0.0001474855,0.00004649132,0.0001960384,0.000009158221],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9743363,0.001729131,0.001054868,0.000499544,0.0006890838,0.01961181,0.000625581,0.00008965303,0.001363928],"genre_scores_gemma":[0.941588,0.001264615,0.005069847,0.001034828,0.001149564,0.04718316,0.0007535085,0.00002128515,0.001935179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005187872,"threshold_uncertainty_score":0.01750594,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4319340692","doi":"10.1093/ehjdh/ztad010","title":"The association of electronic health literacy with behavioural and psychological coronary artery disease risk factors in patients after percutaneous coronary intervention: a 12-month follow-up study","year":2023,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Public Health Ontario; University of Toronto","funders":"St. Olavs Hospital Universitetssykehuset i Trondheim; Helse Vest","keywords":"eHealth; Medicine; Health literacy; Percutaneous coronary intervention; Conventional PCI; Literacy; The Internet; Population; Psychological intervention; Gerontology; Physical therapy; Internal medicine; Health care; Psychiatry; Myocardial infarction; Environmental health; World Wide Web; Psychology","authors":[{"name":"Gunhild Brørs","is_ca":false},{"name":"Håvard Dalen","is_ca":false},{"name":"Heather Allore","is_ca":false},{"name":"Christi Deaton","is_ca":false},{"name":"Bengt Fridlund","is_ca":false},{"name":"Cameron D. Norman","is_ca":true},{"name":"Pernille Palm","is_ca":false},{"name":"Tore Wentzel‐Larsen","is_ca":false},{"name":"Tone M. Norekvål","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04854058281020193,"gpt":0.3972469304124254,"spread":0.3487063476022235,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001319101,0.0004002526,0.0008489306,0.001122434,0.0008385581,0.00115695,0.0004230362,0.001551396,0.002367945],"category_scores_gemma":[0.004469918,0.0004594727,0.001156077,0.001193787,0.0003069396,0.001009597,0.0007793197,0.00211392,0.0006540041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004532266,"about_ca_system_score_gemma":0.0003962691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005103102,"about_ca_topic_score_gemma":0.005600386,"domain_scores_codex":[0.9992511,0.0001783205,0.0001022206,0.0001212109,0.0001546509,0.0001925824],"domain_scores_gemma":[0.9969705,0.0004699256,0.001150797,0.0001886113,0.0004842121,0.0007359069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005456813,0.0008605312,0.9959897,0.00002337029,0.0001354857,0.00007668175,0.0002265659,0.00002641612,0.0001108315,0.000005607404,0.0001133978,0.001885668],"study_design_scores_gemma":[0.00002596742,0.000579149,0.9989156,0.000007077843,0.00005291723,0.00005758514,0.0001515399,0.00008069699,0.00003090449,0.000008611143,0.00008377135,0.000006269091],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989613,0.0002042145,0.00004939419,0.0000684153,0.000007807659,0.00004567658,0.0002972129,0.000004367433,0.0003614363],"genre_scores_gemma":[0.9990231,0.0000799784,0.00006604574,0.0000498181,0.00001442337,0.00004760114,0.000385902,0.000002003632,0.000331172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005103102,"threshold_uncertainty_score":0.0101468,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4407429394","doi":"10.1093/ehjdh/ztaf006","title":"Machine-learning phenotyping of patients with functional mitral regurgitation undergoing transcatheter edge-to-edge repair: the MITRA-AI study","year":2025,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Surgical Specialties (Canada)","funders":"","keywords":"Clinical endpoint; Medicine; Mitral regurgitation; Internal medicine; Cardiology; Cohort; Ejection fraction; Heart failure; Functional mitral regurgitation; Atrial fibrillation; Clinical trial","authors":[{"name":"Fabrizio D’Ascenzo","is_ca":false},{"name":"Filippo Angelini","is_ca":false},{"name":"Corrado Pancotti","is_ca":false},{"name":"Pier Paolo Bocchino","is_ca":false},{"name":"Cristina Giannini","is_ca":false},{"name":"Filippo Finizio","is_ca":false},{"name":"Marianna Adamo","is_ca":false},{"name":"Victoria Lucia Camman","is_ca":false},{"name":"Nuccia Morici","is_ca":false},{"name":"Leor Perl","is_ca":false},{"name":"Saverio Muscoli","is_ca":false},{"name":"Gabriele Crimi","is_ca":false},{"name":"Paolo Boretto","is_ca":false},{"name":"Ovidio De Filippo","is_ca":false},{"name":"Luca Baldetti","is_ca":false},{"name":"Giuseppe Biondi‐Zoccai","is_ca":false},{"name":"Federico Conrotto","is_ca":false},{"name":"Sonia Petronio","is_ca":false},{"name":"Arturo Giordano","is_ca":false},{"name":"Rodrigo Estévez-Loureiro","is_ca":false},{"name":"Davide Stolfo","is_ca":false},{"name":"Christian Templin","is_ca":false},{"name":"Mauro Chiarito","is_ca":true},{"name":"Elena Cavallone","is_ca":false},{"name":"Veronica Dusi","is_ca":false},{"name":"Gianluca Alunni","is_ca":false},{"name":"Jacopo Oreglia","is_ca":false},{"name":"Mario Iannaccone","is_ca":false},{"name":"Marco Pocar","is_ca":false},{"name":"Matteo Pagnesi","is_ca":false},{"name":"Stefano Pidello","is_ca":false},{"name":"Ran Kornowski","is_ca":false},{"name":"Piero Fariselli","is_ca":false},{"name":"Simone Frea","is_ca":false},{"name":"Michele La Torre","is_ca":false},{"name":"Claudia Raineri","is_ca":false},{"name":"Giuseppe Patti","is_ca":false},{"name":"Italo Porto","is_ca":false},{"name":"Antonio Montefusco","is_ca":false},{"name":"Sergio Raposeiras Roubin","is_ca":false},{"name":"Gaetano Maria De Ferrari","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0205815233149,"gpt":0.3206287653366506,"spread":0.3000472420217506,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001505746,0.000497904,0.0005432418,0.0005802856,0.0002843487,0.0007560463,0.0005756168,0.0006450499,0.0007984012],"category_scores_gemma":[0.004144191,0.0002336372,0.0004954687,0.0004723692,0.0002754763,0.0006494775,0.0006456015,0.0006203635,0.000286687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001660033,"about_ca_system_score_gemma":0.0001722111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005349967,"about_ca_topic_score_gemma":0.0007200921,"domain_scores_codex":[0.999257,0.0002854774,0.00007156168,0.000250635,0.0000703999,0.00006487559],"domain_scores_gemma":[0.9973752,0.0006307368,0.0009124762,0.0005366289,0.0001770902,0.000367767],"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":[0.0007440041,0.0001109569,0.9960245,0.00001033006,0.0001274045,0.00006563309,0.00004329151,0.0001771955,0.0004631851,0.00003590974,0.0001875846,0.00200998],"study_design_scores_gemma":[0.00006087092,0.0003875731,0.9965077,0.000006572253,0.00008344709,0.0002685932,0.00007044482,0.002186733,0.0001384875,0.00008085356,0.0001989245,0.000009692803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992544,0.00006969062,0.0002172168,0.00003308697,0.000004632616,0.000007549535,0.0002904909,0.000003346123,0.0001195668],"genre_scores_gemma":[0.9987424,0.00003333069,0.0002250572,0.00002811231,0.00002511842,0.00001202495,0.0008654438,0.000002649748,0.00006598249],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001505746,"threshold_uncertainty_score":0.00796324,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406895577","doi":"10.1093/ehjdh/ztaf005","title":"Artificial intelligence for the analysis of intracoronary optical coherence tomography images: a systematic review","year":2025,"lang":"en","type":"review","venue":"European Heart Journal - Digital Health","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University; Population Health Research Institute; Hamilton General Hospital; McGill University; Hamilton Health Sciences; Royal Victoria Hospital","funders":"Ministerie van Economische Zaken en Klimaat; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Optical coherence tomography; Computer science; Artificial intelligence; Revascularization; Interpretation (philosophy); Medical physics; Radiology; Medicine; Cardiology","authors":[{"name":"Ruben van der Waerden","is_ca":false},{"name":"Rick Volleberg","is_ca":false},{"name":"Thijs Luttikholt","is_ca":false},{"name":"Pierandrea Cancian","is_ca":false},{"name":"Joske van der Zande","is_ca":false},{"name":"Gregg W. Stone","is_ca":false},{"name":"Niels Ramsing Holm","is_ca":false},{"name":"Elvin Kedhi","is_ca":true},{"name":"Javier Escaned","is_ca":false},{"name":"Dario Pellegrini","is_ca":false},{"name":"Giulio Guagliumi","is_ca":false},{"name":"Shamir R. Mehta","is_ca":true},{"name":"Natalia Pinilla‐Echeverri","is_ca":true},{"name":"Raúl Moreno","is_ca":false},{"name":"Lorenz Räber","is_ca":false},{"name":"Tomasz Roleder","is_ca":false},{"name":"Bram van Ginneken","is_ca":false},{"name":"Clara I. Sánchez","is_ca":false},{"name":"Ivana Išgum","is_ca":false},{"name":"Niels van Royen","is_ca":false},{"name":"Jos Thannhauser","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1113657544975806,"gpt":0.4274971845208105,"spread":0.3161314300232299,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00818381,0.001450595,0.005137451,0.008555972,0.0003615492,0.002272417,0.001792284,0.001485171,0.004071102],"category_scores_gemma":[0.04200128,0.0006900951,0.00701418,0.006870526,0.000647356,0.001727272,0.001150901,0.001081765,0.0003833002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001932467,"about_ca_system_score_gemma":0.007742591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005879324,"about_ca_topic_score_gemma":0.01498104,"domain_scores_codex":[0.9948756,0.002033286,0.001526241,0.0003770119,0.001097727,0.00009010522],"domain_scores_gemma":[0.9589603,0.03503422,0.003241351,0.0004865078,0.002103135,0.0001744872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001247854,0.00003722138,0.0007505463,0.8062754,0.01065652,0.00007445443,0.00009747728,0.000586342,0.000113371,0.0007156939,0.002690209,0.1778781],"study_design_scores_gemma":[0.0002040405,0.000299249,0.004552942,0.8577229,0.08440782,0.0004541379,0.000165854,0.001081747,0.0002512687,0.00194083,0.04883581,0.00008331343],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002404504,0.9990151,0.0002510738,0.0001692677,0.00004450758,0.00006849706,0.00009225986,0.000005253803,0.0001136572],"genre_scores_gemma":[0.004368533,0.9944403,0.0006999754,0.0001873653,0.00004970093,0.0001272654,0.00008144949,0.000003305096,0.00004210194],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.008555972,"threshold_uncertainty_score":0.0432806,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410588928","doi":"10.1093/ehjdh/ztaf009","title":"Examination of the performance of machine learning-based automated coronary plaque characterization by near-infrared spectroscopy–intravascular ultrasound and optical coherence tomography with histology","year":2025,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"British Heart Foundation","keywords":"Optical coherence tomography; Intravascular ultrasound; Medicine; Histology; Radiology; Biomedical engineering; Materials science; Pathology","authors":[{"name":"Retesh Bajaj","is_ca":true},{"name":"Ramya Parasa","is_ca":false},{"name":"Alexander Broersen","is_ca":false},{"name":"Tom Johnson","is_ca":false},{"name":"Mohil Garg","is_ca":false},{"name":"Francesco Prati","is_ca":false},{"name":"Murat Çap","is_ca":false},{"name":"Nathan Angelo Lecaros Yap","is_ca":false},{"name":"Medeni Karaduman","is_ca":false},{"name":"Carol A.G.R. Busk","is_ca":false},{"name":"Stephanie Grainger","is_ca":false},{"name":"Steven A. White","is_ca":false},{"name":"Anthony Mathur","is_ca":false},{"name":"Héctor M. García‐García","is_ca":false},{"name":"Jouke Dijkstra","is_ca":false},{"name":"Ryo Torii","is_ca":false},{"name":"Andreas Baumbach","is_ca":false},{"name":"Helle Precht","is_ca":false},{"name":"Christos V. Bourantas","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008423267700267857,"gpt":0.2551061414386032,"spread":0.2466828737383354,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01424193,0.0007178433,0.0005268661,0.002776166,0.0003851083,0.001210933,0.0006351764,0.001243299,0.001016782],"category_scores_gemma":[0.02322249,0.0003648185,0.0005537117,0.0006194787,0.0006123016,0.001044646,0.0007050994,0.0003775936,0.0008072874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006070217,"about_ca_system_score_gemma":0.000393557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002021601,"about_ca_topic_score_gemma":0.001844795,"domain_scores_codex":[0.9953675,0.001882445,0.0003745636,0.001024395,0.001154292,0.0001966984],"domain_scores_gemma":[0.9822469,0.008842106,0.00239979,0.002185331,0.004022893,0.0003029621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006794534,0.0007235088,0.5136197,0.0007326904,0.001562448,0.0002801151,0.0007199676,0.07659566,0.0990854,0.000882745,0.001365976,0.2976373],"study_design_scores_gemma":[0.00007800954,0.002157385,0.4465804,0.00009154626,0.0003519594,0.0005631872,0.000234631,0.5051159,0.04253078,0.0006912129,0.001489318,0.0001156395],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9603228,0.00143725,0.03534044,0.0001099154,0.00005306228,0.00008138664,0.0003202452,0.0004485009,0.001886366],"genre_scores_gemma":[0.9860345,0.0001402531,0.01290692,0.00003533797,0.00002902,0.00003135606,0.0004005293,0.00003420455,0.0003878934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01424193,"threshold_uncertainty_score":0.07531947,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4407871197","doi":"10.1093/ehjdh/ztaf008","title":"An investigation into the causes of race bias in artificial intelligence–based cine cardiac magnetic resonance segmentation","year":2025,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"St. Thomas Hospital","funders":"","keywords":"Segmentation; Artificial intelligence; Interpretability; Computer science; Race (biology); Pattern recognition (psychology)","authors":[{"name":"Tiarna Lee","is_ca":false},{"name":"Esther Puyol‐Antón","is_ca":false},{"name":"Bram Ruijsink","is_ca":true},{"name":"Sébastien Roujol","is_ca":false},{"name":"Theodore Barfoot","is_ca":false},{"name":"Shaheim Ogbomo-Harmitt","is_ca":false},{"name":"Miaojing Shi","is_ca":false},{"name":"Andrew P. King","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06333906124269503,"gpt":0.3621886258317769,"spread":0.2988495645890819,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03063726,0.0006095254,0.0005637858,0.0009345386,0.0007428578,0.00294165,0.001118033,0.001116811,0.001782472],"category_scores_gemma":[0.08751711,0.0003644528,0.0009617378,0.0008294529,0.00154604,0.001889616,0.001168993,0.001439425,0.0005805201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001188846,"about_ca_system_score_gemma":0.00112422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004704595,"about_ca_topic_score_gemma":0.004020116,"domain_scores_codex":[0.9914328,0.005303435,0.0004784597,0.001244168,0.001274072,0.0002671663],"domain_scores_gemma":[0.9141966,0.06299583,0.006829101,0.007944047,0.007454108,0.0005803164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002370277,0.0004263472,0.7008962,0.0004846114,0.001213189,0.0005466847,0.006022916,0.03794563,0.02202154,0.0125858,0.003289313,0.2121975],"study_design_scores_gemma":[0.000133123,0.001058883,0.377562,0.0004606676,0.0005270328,0.00109962,0.001894409,0.5438612,0.03738277,0.02830528,0.007577881,0.0001370891],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9055029,0.001739176,0.0838514,0.003079333,0.0001724571,0.000123807,0.0002658808,0.0004053461,0.004859614],"genre_scores_gemma":[0.9827856,0.0002057683,0.01537991,0.0004210736,0.00008266709,0.00003525932,0.0002891147,0.00009390673,0.0007065489],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03063726,"threshold_uncertainty_score":0.1620273,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4408577077","doi":"10.1093/ehjdh/ztaf018","title":"Racial and ethnic disparities in aortic stenosis within a universal healthcare system characterized by natural language processing for targeted intervention","year":2025,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Network for Business Sustainability","funders":"King's College London; British Heart Foundation; King’s College Hospital Charity; King’s College London; Australian Mammal Society","keywords":"Ethnic group; Intervention (counseling); Health care; Stenosis; Natural (archaeology); Medicine; Healthcare system; Cardiology; Nursing; Sociology; Economic growth; Geography; Economics; Anthropology","authors":[{"name":"Dhruva Biswas","is_ca":false},{"name":"Jack Wu","is_ca":false},{"name":"Samuel Brown","is_ca":false},{"name":"Apurva Bharucha","is_ca":false},{"name":"Natalie Fairhurst","is_ca":false},{"name":"George Kaye","is_ca":false},{"name":"Kate Jones","is_ca":true},{"name":"Freya Parker Copeland","is_ca":true},{"name":"Bethan O’Donnell","is_ca":true},{"name":"Daniel Kyle","is_ca":true},{"name":"Thomas Searle","is_ca":false},{"name":"Nilesh Pareek","is_ca":false},{"name":"Rafał Dworakowski","is_ca":false},{"name":"Alexandros Papachristidis","is_ca":false},{"name":"Narbeh Melikian","is_ca":false},{"name":"Olaf Wendler","is_ca":false},{"name":"Ranjit Deshpande","is_ca":false},{"name":"Max Baghai","is_ca":false},{"name":"James Galloway","is_ca":false},{"name":"James Teo","is_ca":false},{"name":"Richard Dobson","is_ca":false},{"name":"Jonathan Byrne","is_ca":false},{"name":"Philip MacCarthy","is_ca":false},{"name":"Ajay M. Shah","is_ca":false},{"name":"Mehdi Eskandari","is_ca":false},{"name":"Kevin O’Gallagher","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01997162733823016,"gpt":0.3693509752662998,"spread":0.3493793479280697,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00154867,0.0001679587,0.0001755898,0.0006723291,0.0005643764,0.0009337701,0.0002175923,0.000221232,0.001869772],"category_scores_gemma":[0.004841617,0.0001321396,0.0003320333,0.0007654684,0.0004642204,0.0004402125,0.00102547,0.0002545587,0.0001589622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000437505,"about_ca_system_score_gemma":0.0007505782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01715506,"about_ca_topic_score_gemma":0.02559542,"domain_scores_codex":[0.9990151,0.0004428507,0.0000789772,0.0002060633,0.0001278808,0.0001291293],"domain_scores_gemma":[0.9983578,0.0005557283,0.0006988278,0.0001347389,0.000121761,0.0001311039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008510784,0.00005120464,0.9904597,0.00002854997,0.00006147598,0.00006851022,0.001294614,0.0004772804,0.0008536308,0.0003841908,0.0002541403,0.005981669],"study_design_scores_gemma":[0.000004745115,0.00004307963,0.99378,0.00002115552,0.00002057547,0.0000815813,0.001842675,0.003111739,0.0001964455,0.0004569545,0.0004340111,0.000007064357],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982822,0.00005464044,0.0005329669,0.0002468233,0.00000288004,0.00001152598,0.0003034348,0.000006831883,0.0005587979],"genre_scores_gemma":[0.999319,0.00002841181,0.0003659685,0.00004691746,0.000004041486,0.000006536224,0.0001745856,0.00000201991,0.0000525605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01715506,"threshold_uncertainty_score":0.03411037,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4391919090","doi":"10.1093/ehjdh/ztae012","title":"Impact of age and comorbid heart failure on the utility of smart voice-assistant devices","year":2024,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"AI in Service Interactions","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Montreal Heart Institute; McGill University; Université de Montréal; McGill University Health Centre","funders":"McGill University Health Centre; McGill University; Amazon Web Services; American Welding Society","keywords":"Concordance; Medicine; Subgroup analysis; Population; Heart failure; Gerontology; Audiology; Physical therapy; Family medicine; Internal medicine; Confidence interval","authors":[{"name":"Pedro Marques","is_ca":true},{"name":"Anahita Emami","is_ca":true},{"name":"Guang Zhang","is_ca":true},{"name":"Renato D. Lópes","is_ca":false},{"name":"Amir Razaghizad","is_ca":true},{"name":"Robert Avram","is_ca":true},{"name":"Abhinav Sharma","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05652378643529181,"gpt":0.3492112892225513,"spread":0.2926875027872595,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01121213,0.0003750508,0.0009215482,0.0006315112,0.0004532157,0.001073848,0.0003431551,0.0005196497,0.002493893],"category_scores_gemma":[0.04877141,0.0002127157,0.001595074,0.0004484613,0.0004291727,0.0009680875,0.001014683,0.0004841039,0.0002387654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003269456,"about_ca_system_score_gemma":0.0004911927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00113568,"about_ca_topic_score_gemma":0.001779885,"domain_scores_codex":[0.9865304,0.007611942,0.001952459,0.0009021245,0.00248987,0.0005131259],"domain_scores_gemma":[0.9566033,0.02669784,0.01037301,0.002015921,0.003280174,0.001029687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01438178,0.0004146706,0.9099034,0.0009950324,0.001412834,0.0001327196,0.001432757,0.0003117571,0.001341076,0.00009219837,0.0003643484,0.06921737],"study_design_scores_gemma":[0.0002816014,0.008510105,0.9839169,0.0003519827,0.001255515,0.0004027915,0.001809069,0.0008088374,0.001349255,0.0001543207,0.001115577,0.000043995],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950517,0.002599769,0.0004031461,0.0001260573,0.00003593398,0.00007949782,0.0002139954,0.000006754074,0.001483146],"genre_scores_gemma":[0.9989435,0.000298259,0.0003734137,0.0000583615,0.00002708141,0.00005025757,0.00009353093,0.000003144572,0.000152523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01121213,"threshold_uncertainty_score":0.05929619,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388892778","doi":"10.1093/ehjdh/ztad073","title":"Development and internal validation of machine learning–based models and external validation of existing risk scores for outcome prediction in patients with ischaemic stroke","year":2023,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"National Institute on Minority Health and Health Disparities","keywords":"Medicine; Random forest; Receiver operating characteristic; Support vector machine; Gradient boosting; Predictive modelling; Stroke (engine); Outcome (game theory); Machine learning; Failure to thrive; Artificial intelligence; Internal medicine; Computer science; Engineering; Mathematics","authors":[{"name":"Daniel Axford","is_ca":false},{"name":"Ferdous Sohel","is_ca":false},{"name":"Vida Abedi","is_ca":false},{"name":"Ye Zhu","is_ca":false},{"name":"Ramin Zand","is_ca":false},{"name":"Ebrahim Barkoudah","is_ca":false},{"name":"Troy Krupica","is_ca":false},{"name":"Kingsley Iheasirim","is_ca":false},{"name":"Umesh Sharma","is_ca":false},{"name":"Sagar B. Dugani","is_ca":false},{"name":"Paul Y. Takahashi","is_ca":false},{"name":"Sumit Bhagra","is_ca":false},{"name":"M. Hassan Murad","is_ca":false},{"name":"Gustavo Saposnik","is_ca":true},{"name":"Mohammed Yousufuddin","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0723013014128489,"gpt":0.3224504234544739,"spread":0.250149122041625,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03578182,0.001338615,0.0008054279,0.001198483,0.0004361292,0.001715815,0.001269424,0.0009811443,0.0007785525],"category_scores_gemma":[0.06255688,0.0004314287,0.0015498,0.0007093056,0.0007426448,0.001223031,0.00174462,0.002020334,0.0005907191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009511518,"about_ca_system_score_gemma":0.002133888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001913104,"about_ca_topic_score_gemma":0.002234043,"domain_scores_codex":[0.9887449,0.007054635,0.001020499,0.001149254,0.00163455,0.0003960535],"domain_scores_gemma":[0.9646589,0.02095159,0.003300849,0.003335159,0.007098324,0.0006551452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001557716,0.001463397,0.7691795,0.0002232135,0.00130822,0.000164109,0.0005170686,0.09817722,0.001914853,0.00107495,0.003168985,0.1212509],"study_design_scores_gemma":[0.0004251906,0.002447882,0.2106151,0.0002949209,0.0005343701,0.0003507501,0.0001758304,0.7730217,0.007305025,0.002553455,0.002177141,0.00009863909],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8665413,0.0007306387,0.1268394,0.0008604267,0.0001764668,0.0005817355,0.001061023,0.0005726354,0.002636484],"genre_scores_gemma":[0.972673,0.0001364718,0.02498281,0.000210591,0.00007087462,0.0003591412,0.001210228,0.00004352875,0.0003134172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03578182,"threshold_uncertainty_score":0.1892346,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4411668162","doi":"10.1093/ehjdh/ztaf061","title":"The hope and the hype of artificial intelligence for syncope management","year":2025,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Cardiovascular Syncope and Autonomic Disorders","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa; Libin Cardiovascular Institute of Alberta; Ottawa Public Health; University of Calgary","funders":"","keywords":"Syncope (phonology); Business; Psychology; Medicine; Cardiology","authors":[{"name":"Samuel L. Johnston","is_ca":false},{"name":"Ercole John Barsotti","is_ca":false},{"name":"Konstantinos Bakogiannis","is_ca":false},{"name":"Artur Fedorowski","is_ca":false},{"name":"Fabrizio Ricci","is_ca":false},{"name":"Eric G Heller","is_ca":false},{"name":"Robert S. Sheldon","is_ca":true},{"name":"Richard Sutton","is_ca":false},{"name":"Win‐Kuang Shen","is_ca":false},{"name":"Venkatesh Thiruganasambandamoorthy","is_ca":true},{"name":"Mehul Adhaduk","is_ca":false},{"name":"William H. Parker","is_ca":false},{"name":"Arwa Aburizik","is_ca":false},{"name":"Corey R Haselton","is_ca":false},{"name":"Alex J Cuskey","is_ca":false},{"name":"Sangil Lee","is_ca":false},{"name":"Madeleine Johansson","is_ca":false},{"name":"Donald E. Macfarlane","is_ca":false},{"name":"Paari Dominic","is_ca":false},{"name":"Haruhiko Abe","is_ca":false},{"name":"B. Hygriv Rao","is_ca":false},{"name":"Avinash Mudireddy","is_ca":false},{"name":"Milan Sonka","is_ca":false},{"name":"Roopinder K. Sandhu","is_ca":true},{"name":"Rose Anne Kenny","is_ca":false},{"name":"Giselle M. Statz","is_ca":false},{"name":"Rakesh Gopinathannair","is_ca":false},{"name":"David G. Benditt","is_ca":false},{"name":"Franca Dipaola","is_ca":false},{"name":"Mauro Gatti","is_ca":false},{"name":"Roberto Menè","is_ca":false},{"name":"Alessandro Giaj Levra","is_ca":false},{"name":"Dana Shiffer","is_ca":false},{"name":"Giorgio Costantino","is_ca":false},{"name":"Raffaello Furlan","is_ca":false},{"name":"Martin H. Ruwald","is_ca":false},{"name":"Vassilios Vassilikos","is_ca":false},{"name":"Milena A. Gebska","is_ca":false},{"name":"Brian Olshansky","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02663198031836012,"gpt":0.3137272678009762,"spread":0.2870952874826161,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0258302,0.001055162,0.001422061,0.003121757,0.00170757,0.008135048,0.00168991,0.006437184,0.003434902],"category_scores_gemma":[0.04240413,0.0003920838,0.001548157,0.001435136,0.01938326,0.01460386,0.004225474,0.01244848,0.001392173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003112695,"about_ca_system_score_gemma":0.004260729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002179834,"about_ca_topic_score_gemma":0.001386591,"domain_scores_codex":[0.9847552,0.01046614,0.0007722774,0.001044817,0.002464519,0.0004969933],"domain_scores_gemma":[0.9343421,0.05401841,0.001796109,0.003609059,0.00455019,0.001684053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003616238,0.000232951,0.003740265,0.004102953,0.000456637,0.0004654487,0.003669246,0.002804873,0.0009066898,0.4970202,0.1089644,0.3772747],"study_design_scores_gemma":[0.00006945784,0.0002317658,0.001912555,0.005308248,0.0001092789,0.0005314835,0.001819392,0.003044544,0.0005491271,0.7482533,0.2380498,0.0001211422],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.003292798,0.3383284,0.02680651,0.6084797,0.006346635,0.00004720612,0.0001417502,0.0001976086,0.0163594],"genre_scores_gemma":[0.2795759,0.4060412,0.06233173,0.2130767,0.03115389,0.0004034179,0.0002514018,0.0002738263,0.006892027],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.0258302,"threshold_uncertainty_score":0.1366048,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410191686","doi":"10.1093/ehjdh/ztaf047","title":"A deep learning phenome wide association study of the electrocardiogram","year":2025,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; Bristol-Myers Squibb Canada; Norges Idrettshøgskole; Johnson and Johnson; Boston Scientific Corporation; National Institutes of Health; Apple","keywords":"Phenome; Association (psychology); Artificial intelligence; Computer science; Medicine; Psychology; Biology","authors":[{"name":"J. Weston Hughes","is_ca":false},{"name":"John Theurer","is_ca":false},{"name":"Miloš Vukadinovic","is_ca":false},{"name":"Albert J. Rogers","is_ca":false},{"name":"Sulaiman Somani","is_ca":false},{"name":"Guson Kang","is_ca":false},{"name":"Zaniar Ghazizadeh","is_ca":false},{"name":"Jack W. O’Sullivan","is_ca":false},{"name":"Sneha S. Jain","is_ca":false},{"name":"Bruna Gomes","is_ca":false},{"name":"Michael Salerno","is_ca":false},{"name":"Euan A. Ashley","is_ca":false},{"name":"James Zou","is_ca":false},{"name":"Marco Pérez","is_ca":false},{"name":"David Ouyang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01768993546488477,"gpt":0.3133076825284766,"spread":0.2956177470635918,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00318502,0.0005933944,0.0005032368,0.0007504251,0.0003509742,0.0006825019,0.000623173,0.0007608649,0.0012246],"category_scores_gemma":[0.007208181,0.0002388289,0.0006265709,0.0006016157,0.0003321749,0.0004269264,0.0008732475,0.001507518,0.0002676872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004822042,"about_ca_system_score_gemma":0.0006745009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004407842,"about_ca_topic_score_gemma":0.005913613,"domain_scores_codex":[0.9989688,0.000489008,0.00004898886,0.0003019845,0.00009716042,0.0000940966],"domain_scores_gemma":[0.997604,0.001475355,0.0002148861,0.0003147213,0.0002023689,0.0001887516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001959987,0.001244585,0.8286561,0.0002031544,0.001601711,0.0007886782,0.0001651177,0.0457131,0.006634424,0.00135672,0.00680908,0.1048673],"study_design_scores_gemma":[0.0002619323,0.0007163866,0.2560744,0.00007674979,0.000459545,0.0007005834,0.0001342441,0.7304685,0.002720647,0.005668379,0.002673636,0.00004503818],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9689062,0.0007853586,0.02523259,0.001597843,0.00009255722,0.00005955678,0.002257595,0.0001737901,0.000894428],"genre_scores_gemma":[0.9905499,0.0001659055,0.006127136,0.0002114267,0.00004375663,0.00003417989,0.002279193,0.00001049756,0.0005780054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004407842,"threshold_uncertainty_score":0.01684421,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4403736505","doi":"10.1093/ehjdh/ztae079","title":"Fine-tuned large language models can generate expert-level echocardiography reports","year":2024,"lang":"en","type":"editorial","venue":"European Heart Journal - Digital Health","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Montreal Heart Institute","funders":"Institut de Cardiologie de Montréal; Fondation Institut de Cardiologie de Montréal; Canadian Institute for Advanced Research","keywords":"Computer science; Natural language processing","authors":[{"name":"A. Sowa","is_ca":true},{"name":"Robert Avram","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03605042373573915,"gpt":0.3149609740743287,"spread":0.2789105503385896,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005882887,0.001481643,0.001252476,0.0009853893,0.0003672979,0.003291812,0.002136889,0.0041783,0.01528091],"category_scores_gemma":[0.0496736,0.00071916,0.001847625,0.0002513511,0.0007225805,0.001938796,0.00116386,0.005350626,0.007293927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006679298,"about_ca_system_score_gemma":0.0008518801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008142561,"about_ca_topic_score_gemma":0.002633705,"domain_scores_codex":[0.9978593,0.0008998204,0.0002376063,0.0001938631,0.0007457368,0.00006376208],"domain_scores_gemma":[0.9505343,0.04193992,0.000726707,0.00137585,0.004677517,0.0007456642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004051265,0.00006255403,0.0003946784,0.0006366057,0.0003552624,0.0005228845,0.00005717672,0.01108622,0.001525909,0.002510423,0.8683186,0.1141246],"study_design_scores_gemma":[0.001008148,0.0001657485,0.001063828,0.0009215705,0.000677562,0.0007456518,0.0000843002,0.1596791,0.003733808,0.03788732,0.7938146,0.0002183434],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.004481526,0.0082774,0.284257,0.09108263,0.5760538,0.000331278,0.003417653,0.02192806,0.01017075],"genre_scores_gemma":[0.09865267,0.01380602,0.1943506,0.05814695,0.5620127,0.0009060377,0.006542031,0.01031368,0.05526935],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.01528091,"threshold_uncertainty_score":0.05111969,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4411187709","doi":"10.1093/ehjdh/ztaf063","title":"Effect of a digital health intervention on outpatients with heart failure: a randomized, controlled trial","year":2025,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Heart Failure Treatment and Management","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"SickKids Foundation","funders":"Rannís; Landspítali Háskólasjúkrahús; Icelandic Centre for Research","keywords":"Heart failure; Randomized controlled trial; Intervention (counseling); Medicine; Physical therapy; Digital health; Psychology; Nursing; Health care; Internal medicine; Political science","authors":[{"name":"Davíð O. Arnar","is_ca":false},{"name":"Bartosz Dobies","is_ca":true},{"name":"Elías F. Guðmundsson","is_ca":true},{"name":"Heida B. Bragadóttir","is_ca":true},{"name":"Gudbjorg Jona Gudlaugsdottir","is_ca":false},{"name":"Auður Ketilsdóttir","is_ca":false},{"name":"Hallveig Broddadottir","is_ca":false},{"name":"Brynja Laxdal","is_ca":false},{"name":"Þórdís Jóna Hrafnkelsdóttir","is_ca":false},{"name":"Inga Jóna Ingimarsdóttir","is_ca":false},{"name":"Bylgja Kaernested","is_ca":false},{"name":"Axel F. Sigurðsson","is_ca":false},{"name":"Ari Páll Ísberg","is_ca":true},{"name":"Svala Sigurdardottir","is_ca":true},{"name":"Tryggvi Þorgeirsson","is_ca":true},{"name":"Saemundur Oddsson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01412803283190781,"gpt":0.3234214515458203,"spread":0.3092934187139125,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002301688,0.001305079,0.00354511,0.000858899,0.0008498387,0.001343478,0.0009223163,0.002767039,0.01031584],"category_scores_gemma":[0.004521228,0.0005993907,0.002675159,0.0008106356,0.001181917,0.001022167,0.000763607,0.002192943,0.0006999903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000959628,"about_ca_system_score_gemma":0.001690668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002179021,"about_ca_topic_score_gemma":0.00293233,"domain_scores_codex":[0.9979145,0.001090198,0.0002921016,0.000339686,0.0001738166,0.0001897749],"domain_scores_gemma":[0.9977096,0.000891652,0.0005313442,0.000109527,0.0001540292,0.0006038399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","study_design_scores_codex":[0.9660985,0.01139574,0.0006983369,0.002856188,0.002029106,0.00004154301,0.00004602463,0.000149857,0.0004400249,0.00009542357,0.0005048602,0.01564434],"study_design_scores_gemma":[0.9780443,0.01954549,0.001008142,0.0001598162,0.0007460217,0.000008658841,0.00001667724,0.0001290333,0.00007272949,0.00005001872,0.0002126638,0.000006365077],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9562786,0.01735363,0.001135672,0.001889423,0.002354786,0.0154054,0.001498601,0.0002239374,0.00385996],"genre_scores_gemma":[0.9671401,0.005451427,0.002702871,0.001842572,0.001205049,0.01897843,0.0007495616,0.00001432686,0.001915724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01031584,"threshold_uncertainty_score":0.0345099,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4412600169","doi":"10.1093/ehjdh/ztaf083","title":"Artificial intelligence-driven electrocardiogram analysis for risk stratification in pulmonary embolism","year":2025,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"North York General Hospital; Artificial Intelligence in Medicine (Canada); University of Toronto","funders":"National Institute of General Medical Sciences; Center for Research Computing, University of Pittsburgh; National Heart, Lung, and Blood Institute; University of Pittsburgh; National Science Foundation","keywords":"Risk stratification; Pulmonary embolism; Stratification (seeds); Cardiology; Internal medicine; Artificial intelligence; Medicine; Computer science; Biology","authors":[{"name":"Tanmay Gokhale","is_ca":false},{"name":"Nathan T. Riek","is_ca":false},{"name":"Brent Medoff","is_ca":false},{"name":"Rui Qi Ji","is_ca":true},{"name":"Belinda Rivera‐Lebron","is_ca":false},{"name":"Ervin Sejdić","is_ca":true},{"name":"Murat Akçakaya","is_ca":false},{"name":"Samir Saba","is_ca":false},{"name":"Salah S. Al‐Zaiti","is_ca":false},{"name":"Catalin Toma","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04538234022334274,"gpt":0.3536731328578321,"spread":0.3082907926344893,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002259842,0.000654352,0.0005473502,0.001067633,0.0001849095,0.000600977,0.0005600084,0.0005311253,0.0006072356],"category_scores_gemma":[0.007617839,0.0001516171,0.0005226312,0.0004552297,0.0002255455,0.0003264846,0.0003240915,0.0007145436,0.0002276173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005141292,"about_ca_system_score_gemma":0.0006125272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004180779,"about_ca_topic_score_gemma":0.003155755,"domain_scores_codex":[0.9993963,0.0003262963,0.00004473311,0.00008451977,0.0001184026,0.00002975455],"domain_scores_gemma":[0.9968693,0.002343498,0.0002653631,0.0001164556,0.0003320625,0.00007344852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0007637747,0.0007203563,0.1076743,0.0002110022,0.0006353816,0.0003604224,0.0001279912,0.5779737,0.006015824,0.001322133,0.003511049,0.3006841],"study_design_scores_gemma":[0.00002370418,0.0001393276,0.007903413,0.00001917146,0.00003805013,0.00007509773,0.00001021106,0.9894052,0.0007059451,0.001448703,0.0002191052,0.00001195251],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5101421,0.00252975,0.4785535,0.00194334,0.00019931,0.0004545324,0.001198419,0.002216225,0.002762855],"genre_scores_gemma":[0.9476505,0.000261697,0.0507411,0.0002171248,0.00006249755,0.0001157619,0.0005858827,0.00001770507,0.0003477119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004180779,"threshold_uncertainty_score":0.01195127,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388184188","doi":"10.1093/ehjdh/ztad067","title":"Cardiology professionals’ views of social robots in augmenting heart failure patient care","year":2023,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Mechanical Circulatory Support Devices","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; Ottawa Hospital; Wilfrid Laurier University; University of Ottawa","funders":"University of Ottawa; Oracle","keywords":"Medicine; Heart failure; Cardiology; Robot; Intensive care medicine; Medical emergency; Internal medicine; Artificial intelligence","authors":[{"name":"Karen Bouchard","is_ca":true},{"name":"Peter P. Liu","is_ca":true},{"name":"Kerstin Dautenhahn","is_ca":true},{"name":"Jess G. Fiedorowicz","is_ca":true},{"name":"Michael Dans","is_ca":true},{"name":"Caroline McGuinty","is_ca":true},{"name":"Heather Tulloch","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04763480954619882,"gpt":0.3129958880750097,"spread":0.2653610785288109,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01617206,0.0003542646,0.0002447963,0.0007467114,0.005789168,0.005138063,0.0006350221,0.002968754,0.003336344],"category_scores_gemma":[0.02135016,0.000288552,0.000431615,0.0003103441,0.009381062,0.002291023,0.00423968,0.002460802,0.000351773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003100771,"about_ca_system_score_gemma":0.003489254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003475828,"about_ca_topic_score_gemma":0.003867857,"domain_scores_codex":[0.9770151,0.02022499,0.0002960646,0.0005214562,0.001001161,0.0009411838],"domain_scores_gemma":[0.9774694,0.0160914,0.001905621,0.0003764485,0.00135858,0.002798611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00009963732,0.0001340616,0.01544422,0.0003430416,0.00003151079,0.0009201352,0.954491,0.0001504965,0.001150314,0.005737498,0.004520952,0.01697721],"study_design_scores_gemma":[0.00004030385,0.0002554451,0.005775358,0.0005063319,0.00003076351,0.0005845234,0.9587967,0.0003692574,0.0004298979,0.001423788,0.03174544,0.00004218827],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9197493,0.002005068,0.004315293,0.04424537,0.0004412762,0.0001176606,0.00005982233,0.0000308239,0.02903551],"genre_scores_gemma":[0.9936609,0.0005294275,0.0005824833,0.004180541,0.000059971,0.00004342974,0.000008037456,0.000007295804,0.0009279102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01617206,"threshold_uncertainty_score":0.085527,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3174952152","doi":"10.1093/ehjdh/ztab056","title":"Wearables for cardiac monitoring in athletes: precious metal or fool’s gold?","year":2021,"lang":"en","type":"editorial","venue":"European Heart Journal - Digital Health","topic":"Cardiovascular Effects of Exercise","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; St. Michael's Hospital; Western University","funders":"","keywords":"Medicine; Athletes; Wearable computer; Cardiology; Internal medicine; Physical therapy; Embedded system","authors":[{"name":"Yehia Fanous","is_ca":true},{"name":"Paul Dorian","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03764696446877958,"gpt":0.3434399096485602,"spread":0.3057929451797806,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009611481,0.004182218,0.005739752,0.00361415,0.003479644,0.01080105,0.00470137,0.03340974,0.01509578],"category_scores_gemma":[0.03195447,0.001451084,0.00414989,0.001501641,0.003030004,0.005723665,0.002020523,0.02686554,0.009566314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002519539,"about_ca_system_score_gemma":0.003017912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001833039,"about_ca_topic_score_gemma":0.005543379,"domain_scores_codex":[0.9930897,0.001300653,0.001059407,0.0008624471,0.003271483,0.0004162877],"domain_scores_gemma":[0.9690664,0.01593514,0.001524306,0.000682821,0.009008388,0.003782972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008288524,0.00003188354,0.00004102009,0.0006271129,0.00006162698,0.000166816,0.00002548857,0.00001640119,0.00007075352,0.0003782117,0.9881193,0.0103787],"study_design_scores_gemma":[0.0002070113,0.0001166686,0.000545025,0.002084271,0.0002551516,0.0004440864,0.000157061,0.0001962197,0.0001377617,0.001541382,0.994274,0.00004135297],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00003620149,0.01095852,0.0001019745,0.02614989,0.961738,0.00001777718,0.00003575435,0.00003563983,0.0009262585],"genre_scores_gemma":[0.000226176,0.006200712,0.00007595064,0.02002217,0.9700297,0.00001981665,0.00001485419,0.00001780251,0.003392915],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.03340974,"threshold_uncertainty_score":0.05083102,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4391445827","doi":"10.1093/ehjdh/ztae005","title":"Predicting heart failure outcomes by integrating breath-by-breath measurements from cardiopulmonary exercise testing and clinical data through a deep learning survival neural network","year":2024,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Cardiovascular and exercise physiology","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; Ted Rogers Centre for Heart Research; University Health Network","funders":"University Health Network","keywords":"Machine learning; Medicine; Predictive modelling; Artificial intelligence; Heart failure; Event (particle physics); Cohort; Artificial neural network; Computer science; Internal medicine","authors":[{"name":"Heather J. Ross","is_ca":true},{"name":"Mohammad Peikari","is_ca":true},{"name":"Julie K.K. Vishram‐Nielsen","is_ca":true},{"name":"Chun‐Po Steve Fan","is_ca":true},{"name":"Jason Hearn","is_ca":true},{"name":"Mike Walker","is_ca":true},{"name":"Edgar Crowdy","is_ca":true},{"name":"Ana Carolina Alba","is_ca":true},{"name":"Cedric Manlhiot","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09999185926657315,"gpt":0.3581610837628442,"spread":0.258169224496271,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001709357,0.0007464631,0.0005541996,0.0007130634,0.0001843487,0.0006264186,0.0006087393,0.0005057158,0.0006880026],"category_scores_gemma":[0.003705881,0.000243115,0.0005032327,0.0004889509,0.0003280959,0.0006936107,0.0007609834,0.0008655504,0.0001709652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005795292,"about_ca_system_score_gemma":0.0008910176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004962726,"about_ca_topic_score_gemma":0.00611921,"domain_scores_codex":[0.9996873,0.0000974294,0.00002450058,0.00008919708,0.0000643273,0.00003718887],"domain_scores_gemma":[0.999041,0.0005164795,0.0001776338,0.00005822876,0.000152,0.00005459136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006856141,0.0007947428,0.1772932,0.0001118313,0.0003804146,0.0002267207,0.0001105811,0.610815,0.005216937,0.00117776,0.0018035,0.2013837],"study_design_scores_gemma":[0.00001106683,0.0001053186,0.008233566,0.00001222323,0.00002341668,0.0000306287,0.00001138157,0.9898813,0.0007416509,0.0008293479,0.0001110966,0.000009000116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7635283,0.000789352,0.2316134,0.0009393666,0.00007505139,0.0001016749,0.001041521,0.0005955964,0.001315701],"genre_scores_gemma":[0.9796004,0.0001495705,0.01894026,0.0001050064,0.00002954811,0.00006009618,0.0006424452,0.000009674836,0.0004630663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004962726,"threshold_uncertainty_score":0.009867668,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400578756","doi":"10.1093/ehjdh/ztae051","title":"Machine learning-based prediction of 1-year all-cause mortality in patients undergoing CRT implantation: validation of the SEMMELWEIS-CRT score in the European CRT Survey I dataset","year":2024,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Cardiac pacing and defibrillation studies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Nemzeti Kutatási Fejlesztési és Innovációs Hivatal; Nemzeti Kutatási, Fejlesztési és Innovaciós Alap; Ministry of Advanced Education; Magyar Tudományos Akadémia; European Commission","keywords":"Medicine; Cardiac resynchronization therapy; Receiver operating characteristic; Internal medicine; Cohort; Heart failure; Odds ratio; Area under the curve; Ejection fraction; Cardiology","authors":[{"name":"Márton Tokodi","is_ca":false},{"name":"Annamária Kosztin","is_ca":false},{"name":"Attila Kovács","is_ca":false},{"name":"László Gellér","is_ca":false},{"name":"Walter Richárd Schwertner","is_ca":false},{"name":"B Veres","is_ca":false},{"name":"A Behon","is_ca":false},{"name":"Christiane Lober","is_ca":false},{"name":"Nigussie Bogale","is_ca":false},{"name":"Cecilia Linde","is_ca":false},{"name":"Camilla Normand","is_ca":false},{"name":"Kenneth Dickstein","is_ca":false},{"name":"Béla Merkely","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1207262393877813,"gpt":0.3622328724889506,"spread":0.2415066331011693,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005729174,0.0006716528,0.0007742654,0.001160322,0.0002023701,0.000728579,0.0007184302,0.0006106296,0.001097795],"category_scores_gemma":[0.01267883,0.0001261959,0.001121349,0.0006804065,0.000295978,0.0003971675,0.001059331,0.0007762275,0.0005065666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003002031,"about_ca_system_score_gemma":0.0005851089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002519441,"about_ca_topic_score_gemma":0.002963171,"domain_scores_codex":[0.9979811,0.001091619,0.0001878619,0.0003987,0.0002205073,0.0001203208],"domain_scores_gemma":[0.993862,0.003095184,0.0009181001,0.001034488,0.0007029811,0.0003873006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001211446,0.0002760601,0.9611927,0.0001073037,0.0008183271,0.00009278513,0.00006809093,0.01203274,0.0007221811,0.0002009784,0.006096624,0.01718079],"study_design_scores_gemma":[0.0003983945,0.0008365525,0.8861875,0.00008761235,0.0003092835,0.0004763485,0.000116921,0.1066037,0.001148251,0.0006460244,0.003131747,0.00005759694],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9812835,0.0002605107,0.003310968,0.0002359348,0.0000447973,0.00006649732,0.01382802,0.0001473603,0.0008223372],"genre_scores_gemma":[0.9609526,0.00008734647,0.003164944,0.0001328849,0.00004814162,0.00009263219,0.03526399,0.00002370132,0.0002337651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005729174,"threshold_uncertainty_score":0.03029913,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4415482982","doi":"10.1093/ehjdh/ztaf121","title":"Artificial intelligence implementation in automated heart chambers quantification during pharmacological stress echocardiography","year":2025,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Cytodiagnostics (Canada)","funders":"","keywords":"Workflow; Reliability (semiconductor); Heart failure; Matching (statistics); Expert system; Automated method","authors":[{"name":"Arnas Karužas","is_ca":false},{"name":"Quirino Ciampi","is_ca":false},{"name":"Ieva Kažukauskienė","is_ca":false},{"name":"L Miscikas","is_ca":false},{"name":"Karolis Šablauskas","is_ca":false},{"name":"Antanas Kiziela","is_ca":false},{"name":"Dovydas Verikas","is_ca":false},{"name":"Jurgita Plisienė","is_ca":false},{"name":"Vaiva Lesauskaitė","is_ca":false},{"name":"Lauro Cortigiani","is_ca":false},{"name":"Karina Wierzbowska‐Drabik","is_ca":false},{"name":"J D Kasprzak","is_ca":false},{"name":"Jorge Lowenstein","is_ca":true},{"name":"Costantina Prota","is_ca":false},{"name":"Nicola Gaibazzi","is_ca":false},{"name":"Domenico Tuttolomondo","is_ca":false},{"name":"Attilio Lepone","is_ca":false},{"name":"Sofia Marconi","is_ca":true},{"name":"Rosina Arbucci","is_ca":true},{"name":"Eugenio Picano","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0525100625431907,"gpt":0.392811334598755,"spread":0.3403012720555643,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002786131,0.0005638199,0.0005067849,0.0008416224,0.0002253328,0.001119941,0.0008237479,0.0006077873,0.00126209],"category_scores_gemma":[0.01055089,0.0002636563,0.0004540231,0.0006307353,0.000329891,0.000534834,0.0007343625,0.0005655419,0.0004969761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004252223,"about_ca_system_score_gemma":0.0005692613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002174917,"about_ca_topic_score_gemma":0.001710398,"domain_scores_codex":[0.9986449,0.0006888585,0.0001112403,0.0002442622,0.0002293021,0.00008147211],"domain_scores_gemma":[0.995905,0.00270177,0.0003935234,0.0002186858,0.0007089172,0.00007218284],"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":[0.001103745,0.0006061814,0.02807759,0.0003628681,0.0003314039,0.0002288166,0.0004421623,0.1703339,0.03153509,0.001441936,0.001450884,0.7640854],"study_design_scores_gemma":[0.00004141567,0.0003900728,0.01500381,0.00003663732,0.00005811721,0.0001239362,0.00006571339,0.9727885,0.009133581,0.001551036,0.0007814997,0.0000257865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2510558,0.0005187406,0.7425029,0.0002654849,0.00005784724,0.0002865694,0.0002017046,0.002826195,0.002284817],"genre_scores_gemma":[0.748976,0.0001709437,0.2492012,0.0001813354,0.00003955447,0.0003813049,0.0003080621,0.00008021112,0.0006613806],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002786131,"threshold_uncertainty_score":0.01473463,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4200023582","doi":"10.1093/ehjdh/ztab104.3080","title":"Assessment of cognitive dysfunction using the Montreal Cognitive Assessment test: rate, severity and comparison with the Clock test alone in a population of patients referred for TAVI","year":2021,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Cardiac, Anesthesia and Surgical Outcomes","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Montreal Cognitive Assessment; Medicine; Kappa; Internal medicine; Concordance; Interquartile range; Population; Cardiology; Cognitive impairment","authors":[{"name":"Charles Monnin","is_ca":false},{"name":"Matthieu Besutti","is_ca":false},{"name":"Fiona Ecarnot","is_ca":false},{"name":"Benoît Guillon","is_ca":false},{"name":"Marion Chatot","is_ca":false},{"name":"Romain Chopard","is_ca":false},{"name":"M Yahia","is_ca":false},{"name":"Nicolas Méneveau","is_ca":false},{"name":"François Schiele","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04708190296957084,"gpt":0.361291010055323,"spread":0.3142091070857522,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001112454,0.0005682585,0.0006941411,0.002146128,0.0003657239,0.0006681876,0.0003591954,0.0006836135,0.0008716916],"category_scores_gemma":[0.004524079,0.0002144019,0.0005931581,0.0009968845,0.0003606848,0.0006015879,0.0005108244,0.0005092545,0.0002716431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003477806,"about_ca_system_score_gemma":0.0002465159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003716323,"about_ca_topic_score_gemma":0.002807081,"domain_scores_codex":[0.9993481,0.0001675597,0.0001002024,0.000147433,0.0001523761,0.00008433293],"domain_scores_gemma":[0.9973723,0.0006520046,0.0009660378,0.0001268481,0.0003995443,0.0004833302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002370342,0.00005406775,0.9984334,0.000005856433,0.00004057687,0.0001282627,0.00004486955,0.00003477784,0.0001687848,0.00000255976,0.00002888438,0.0008208468],"study_design_scores_gemma":[0.00001542959,0.0005320912,0.9984316,0.00000282158,0.0000223753,0.0004544741,0.00008544044,0.0003558786,0.0000610624,0.000007108066,0.00002585419,0.000005736865],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999631,0.0000885021,0.00005319074,0.000007435327,0.000003972745,0.000007360311,0.00006887662,0.000002474886,0.0001371515],"genre_scores_gemma":[0.9996254,0.00004594328,0.00009480902,0.00001126617,0.00001612644,0.000008956426,0.0001554481,0.000001104229,0.00004084488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003716323,"threshold_uncertainty_score":0.007389367,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3124651630","doi":"10.1093/ehjdh/ztab004","title":"‘How to do’: digital-interactive-interpretation course for stress echocardiography","year":2021,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Ultrasound in Clinical Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta Hospital","funders":"TOMTEC Imaging Systems","keywords":"Medicine; Logbook; Accreditation; Interpretation (philosophy); Medical education; Certificate; Course (navigation); Computer science","authors":[{"name":"Attila Kardos","is_ca":false},{"name":"Ramona Schaupp","is_ca":false},{"name":"Lillian Kettner","is_ca":false},{"name":"Harald Becher","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04487278802020309,"gpt":0.3901220063629869,"spread":0.3452492183427838,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002506023,0.0006601828,0.0002263353,0.0003526132,0.0007113809,0.0007965409,0.001220155,0.001090011,0.04541608],"category_scores_gemma":[0.004542419,0.0001750033,0.0005673557,0.0001463964,0.0005097866,0.00124231,0.00223531,0.001308325,0.01208532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004698535,"about_ca_system_score_gemma":0.001621649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005226763,"about_ca_topic_score_gemma":0.00142784,"domain_scores_codex":[0.9990495,0.0003466739,0.00005805127,0.0001522293,0.000126836,0.0002667144],"domain_scores_gemma":[0.9969234,0.0004471045,0.0001795181,0.0001444556,0.0003098636,0.001995602],"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":[0.0008536966,0.009580307,0.02550424,0.001584399,0.00003264684,0.003667483,0.01063616,0.00111571,0.03028712,0.00241596,0.2562553,0.658067],"study_design_scores_gemma":[0.0005923977,0.008892074,0.1538296,0.001736795,0.0000845149,0.01458293,0.009730306,0.004929605,0.01882542,0.007841236,0.7786013,0.000353915],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6687771,0.001966362,0.1501371,0.03210593,0.00739011,0.008717328,0.002459065,0.00730381,0.1211431],"genre_scores_gemma":[0.6837558,0.001437978,0.2409492,0.01141507,0.001473896,0.002864306,0.001869063,0.0006121488,0.0556226],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04541608,"threshold_uncertainty_score":0.1519319,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400003197","doi":"10.1093/ehjdh/ztae046","title":"Feasibility of anticoagulation on demand after percutaneous coronary intervention in high-bleeding risk patients with paroxysmal atrial fibrillation: the INTERMITTENT registry","year":2024,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Atrial Fibrillation Management and Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Montreal Heart Institute","funders":"","keywords":"Medicine; Paroxysmal atrial fibrillation; Percutaneous coronary intervention; Atrial fibrillation; Internal medicine; Cardiology; Major bleeding; On demand; Percutaneous; Intervention (counseling); Myocardial infarction; Business","authors":[{"name":"Francesco Pelliccia","is_ca":false},{"name":"Marco Zimarino","is_ca":false},{"name":"Melania Giordano","is_ca":false},{"name":"Dobromir Dobrev","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05237946508011087,"gpt":0.3373750106531037,"spread":0.2849955455729928,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00218281,0.0002384562,0.0004451976,0.0008321679,0.0003133084,0.001024507,0.0004882346,0.0005020924,0.001120161],"category_scores_gemma":[0.007726382,0.0002092221,0.0006058967,0.001242482,0.0002972344,0.0005943433,0.000649976,0.0005800464,0.0002547374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002203699,"about_ca_system_score_gemma":0.0003711393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00103593,"about_ca_topic_score_gemma":0.001121592,"domain_scores_codex":[0.9980533,0.0006724865,0.0002818331,0.0004197156,0.0004266943,0.0001459452],"domain_scores_gemma":[0.9870496,0.001871593,0.008118086,0.001432557,0.0007167551,0.0008114885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003077234,0.00003708206,0.9980546,0.000009043491,0.00004893413,0.00003142147,0.00003120313,0.00002244598,0.00006337912,0.00001442237,0.00008026525,0.001299541],"study_design_scores_gemma":[0.00006021562,0.0003435324,0.9986488,0.000006745415,0.00006400025,0.000237516,0.00006678433,0.0002727859,0.00006446094,0.00002587346,0.000204272,0.000005033221],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987801,0.0001966299,0.0001563724,0.00003120729,0.000005370619,0.00001608957,0.0004334011,0.000004305622,0.0003765984],"genre_scores_gemma":[0.9987221,0.0000844779,0.0001519037,0.00002486551,0.00003334552,0.00001975514,0.0008978671,0.000003135563,0.0000624809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00218281,"threshold_uncertainty_score":0.01154393,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4312191841","doi":"10.1093/ehjdh/ztac076.2825","title":"Sustained usage of an app-based clinical-decision making aid for the management of atherosclerotic cardiovascular disease","year":2022,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Cardiac Health and Mental Health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Cambridge Cardiac Care Centre; University of Ottawa","funders":"","keywords":"Medicine; Psychological intervention; Disease; Atherosclerotic cardiovascular disease; Residual risk; Comorbidity; Disease management; Test (biology); Intensive care medicine; Emergency medicine; Physical therapy; Internal medicine; Nursing","authors":[{"name":"A. Shekhar Pandey","is_ca":true},{"name":"Hassan Mir","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07369225856301877,"gpt":0.3925985813010558,"spread":0.3189063227380371,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002899919,0.001198245,0.0006242659,0.0008425668,0.0004005114,0.001685435,0.001147664,0.001182689,0.01655493],"category_scores_gemma":[0.0159091,0.0003565266,0.0005736409,0.000323123,0.0002317553,0.001285477,0.001323584,0.0008058832,0.006569018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002586377,"about_ca_system_score_gemma":0.0006307169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005046074,"about_ca_topic_score_gemma":0.0006460656,"domain_scores_codex":[0.9979601,0.001051621,0.0001950318,0.000343973,0.000322582,0.0001267175],"domain_scores_gemma":[0.9868428,0.01008241,0.0005302561,0.0006883128,0.0009689517,0.0008872153],"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":[0.006067839,0.004720462,0.05275924,0.001655841,0.0003043155,0.002147453,0.002864366,0.003732986,0.008723031,0.001542208,0.1346044,0.7808779],"study_design_scores_gemma":[0.0065519,0.0152339,0.1823809,0.006353152,0.002125131,0.01418213,0.005683069,0.2708013,0.03667267,0.03027434,0.427919,0.00182248],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6268271,0.002137486,0.1960661,0.009389807,0.001207359,0.007651144,0.01259508,0.08948135,0.05464457],"genre_scores_gemma":[0.6923427,0.0008895745,0.2798227,0.003765333,0.0004263168,0.003583563,0.003786633,0.001002663,0.01438062],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01655493,"threshold_uncertainty_score":0.05538172,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4312191769","doi":"10.1093/ehjdh/ztac076.2801","title":"The effectiveness of eHealth interventions on moderate-to-vigorous intensity physical activity among cardiac rehabilitation participants: a systematic review and meta-analysis","year":2022,"lang":"en","type":"review","venue":"European Heart Journal - Digital Health","topic":"Cardiac Health and Mental Health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; eHealth; Rehabilitation; Psychological intervention; Physical therapy; Cochrane Library; Randomized controlled trial; Systematic review; Meta-analysis; MEDLINE; Health care; Internal medicine; Nursing","authors":[{"name":"Tianhua Yu","is_ca":false},{"name":"Rui-Jia Gao","is_ca":true},{"name":"Linqi Xu","is_ca":false},{"name":"Xianwei Zhang","is_ca":false},{"name":"Ting Yu","is_ca":false},{"name":"Xiaoqian Lian","is_ca":false},{"name":"Fudong Li","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1867857310347926,"gpt":0.4620862598370352,"spread":0.2753005288022425,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01547362,0.002493936,0.02379443,0.006587659,0.00070411,0.003517507,0.002481309,0.002587742,0.004221838],"category_scores_gemma":[0.03789853,0.001264182,0.03280929,0.006374537,0.0008519783,0.002284582,0.001567651,0.002116898,0.0002993154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003770728,"about_ca_system_score_gemma":0.004946019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006785149,"about_ca_topic_score_gemma":0.01337675,"domain_scores_codex":[0.9892352,0.004407181,0.003675424,0.0008584735,0.001515981,0.0003077583],"domain_scores_gemma":[0.976626,0.01762288,0.003463975,0.0004660452,0.00155841,0.0002627421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.002554728,0.00006903004,0.001162976,0.6635486,0.3129128,0.00007326832,0.00008636543,0.0002694392,0.0001289858,0.0001124608,0.0004965806,0.0185848],"study_design_scores_gemma":[0.001627488,0.0004688998,0.0027462,0.08544116,0.9074137,0.00007503635,0.00005419633,0.0001852139,0.0001801022,0.0001741864,0.001603835,0.00003000802],"study_design_candidate":"meta_analysis","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003569389,0.9944072,0.0003487996,0.0001525423,0.0001381543,0.0007141379,0.000398017,0.00002159463,0.0002502037],"genre_scores_gemma":[0.1053154,0.8867146,0.002329686,0.0009913886,0.0002671901,0.003323463,0.0006351544,0.00002269981,0.0004003423],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.02379443,"threshold_uncertainty_score":0.0818333,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4415117676","doi":"10.1093/ehjdh/ztaf116","title":"Deep learning-based quantification of epicardial adipose tissue volume from non-contrast computed tomography images: a multi-centre study","year":2025,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Cardiovascular Disease and Adiposity","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Naval Medical Research Center; National Medical Research Council; Medical Research Council; Singapore Energy Centre; Duke-NUS Medical School","keywords":"Computed tomography; Volume (thermodynamics); Adipose tissue; Epicardial adipose tissue; Tomography","authors":[{"name":"Shuang Leng","is_ca":false},{"name":"Nicholas Cheng","is_ca":false},{"name":"Eddy Tan","is_ca":false},{"name":"Lohendran Baskaran","is_ca":false},{"name":"Lynette Teo","is_ca":false},{"name":"Min Sen Yew","is_ca":false},{"name":"Kee Yuan Ngiam","is_ca":false},{"name":"Weimin Huang","is_ca":false},{"name":"Ping Chai","is_ca":false},{"name":"Ching Ching Ong","is_ca":false},{"name":"Ching‐Hui Sia","is_ca":false},{"name":"Malay Singh","is_ca":false},{"name":"Yan Ting Loong","is_ca":false},{"name":"N A S Raffiee","is_ca":false},{"name":"Xiaomeng Wang","is_ca":false},{"name":"John Carson Allen","is_ca":false},{"name":"Swee Yaw Tan","is_ca":false},{"name":"Mark Y. Chan","is_ca":false},{"name":"Hwee Kuan Lee","is_ca":true},{"name":"Liang Zhong","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01761559718864034,"gpt":0.2934292360366167,"spread":0.2758136388479764,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004096983,0.0009754656,0.0006762884,0.0008172815,0.0002734022,0.001288787,0.0009538071,0.0006760632,0.0006789215],"category_scores_gemma":[0.006570191,0.0004419837,0.000906244,0.000636874,0.000614226,0.0007098811,0.0009786834,0.00071719,0.0003521689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005570122,"about_ca_system_score_gemma":0.0004016606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003961849,"about_ca_topic_score_gemma":0.003693074,"domain_scores_codex":[0.9988061,0.0004793807,0.00008133169,0.0004102855,0.0001474599,0.00007553294],"domain_scores_gemma":[0.9962785,0.001065508,0.0007158385,0.0009482821,0.0007166947,0.0002752364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002878316,0.0004961923,0.9056467,0.0002166707,0.001762025,0.0005215646,0.0005757988,0.01922467,0.01487211,0.0003333718,0.0008280573,0.05264456],"study_design_scores_gemma":[0.0002298429,0.001449974,0.7947781,0.00009507948,0.001010663,0.001981105,0.0004876612,0.1873102,0.01006254,0.0008998953,0.001564356,0.0001305491],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912743,0.0003632319,0.007554921,0.00004070101,0.00001028988,0.0000374769,0.0003902693,0.00005649656,0.0002722392],"genre_scores_gemma":[0.996608,0.0000791167,0.002604481,0.0000247181,0.00001249263,0.00002237368,0.0005248992,0.00002086411,0.0001031455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004096983,"threshold_uncertainty_score":0.02166718,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4415022001","doi":"10.1093/ehjdh/ztaf115","title":"Unsupervised machine learning analysis to enhance risk stratification in patients with asymptomatic aortic stenosis","year":2025,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval; Centre hospitalier de l'Université Laval; Institut universitaire de cardiologie et de pneumologie de Québec","funders":"Agence Nationale de la Recherche","keywords":"Risk stratification; Asymptomatic; Risk assessment; Stenosis; Unsupervised learning; Medical imaging","authors":[{"name":"Marie‐Ange Fleury","is_ca":true},{"name":"Louis Ohl","is_ca":false},{"name":"Lionel Tastet","is_ca":false},{"name":"Mickaël Leclercq","is_ca":true},{"name":"Fŕed́eric Precioso","is_ca":false},{"name":"Pierre‐Alexandre Mattei","is_ca":false},{"name":"Romain Capoulade","is_ca":false},{"name":"Kathia Abdoun","is_ca":true},{"name":"Élisabeth Bédard","is_ca":true},{"name":"Marie Arsenault","is_ca":true},{"name":"Jonathan Beaudoin","is_ca":true},{"name":"Mathieu Bernier","is_ca":true},{"name":"Erwan Salaün","is_ca":true},{"name":"Jérémy Bernard","is_ca":true},{"name":"Mylène Shen","is_ca":true},{"name":"Sébastien Hecht","is_ca":true},{"name":"Nancy Côté","is_ca":true},{"name":"Arnaud Droit","is_ca":true},{"name":"Philippe Pîbarot","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01063695023781039,"gpt":0.3348840971275104,"spread":0.3242471468897,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002651103,0.0004778015,0.0007096379,0.001723698,0.0003385982,0.0007073075,0.0004278035,0.000380889,0.001155159],"category_scores_gemma":[0.007173988,0.0001419512,0.0006359872,0.0007191217,0.0002714104,0.0003481207,0.0005206269,0.0004763208,0.0003714488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002824937,"about_ca_system_score_gemma":0.0005882824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001531941,"about_ca_topic_score_gemma":0.001939764,"domain_scores_codex":[0.9986206,0.0008326442,0.0001168428,0.000199207,0.0001410824,0.00008957656],"domain_scores_gemma":[0.9955742,0.002692356,0.0006531753,0.0003211485,0.0005546382,0.0002044339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001659838,0.0005247854,0.8389676,0.0001079609,0.0005124591,0.0002152971,0.0002063422,0.01548871,0.004708645,0.0005011476,0.002102074,0.1350051],"study_design_scores_gemma":[0.0001151153,0.0008895797,0.5856012,0.00007015828,0.0002305959,0.0005401402,0.0002865775,0.4040234,0.002947374,0.003704331,0.001530029,0.00006140541],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9633787,0.0004359455,0.03346277,0.0003908387,0.00004805145,0.0001131184,0.0008810516,0.0002690941,0.001020507],"genre_scores_gemma":[0.982414,0.00007776797,0.01612738,0.0000518595,0.00005123411,0.0000607097,0.0009013963,0.00001690934,0.0002988062],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002651103,"threshold_uncertainty_score":0.01402056,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}