{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":1809,"total_is_capped":false,"direct_labels_cover":15,"predictions_cover":1809,"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":"42d488d18bcf","filters":{"venue":"JMIR Medical Informatics"}},"results":[{"id":"W2991379615","doi":"10.2196/14325","title":"Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":561,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; U.S. National Library of Medicine; National Heart, Lung, and Blood Institute; China Scholarship Council; National Institutes of Health; Cancer Research UK; Georgia Clinical and Translational Science Alliance; Vanderbilt University Medical Center; Vanderbilt University; National Institute of General Medical Sciences; American Heart Association","keywords":"ICD-10; Medicine; Systematized Nomenclature of Medicine; Biorepository; Health informatics; Diagnosis code; Informatics; Medicaid; Concordance; Medical classification; SNOMED CT; Electronic health record; Biobank; Health care; Internal medicine; Public health; Pathology; Terminology; Bioinformatics; Population","authors":[{"name":"Patrick Wu","is_ca":false},{"name":"Aliya Gifford","is_ca":false},{"name":"Xiangrui Meng","is_ca":false},{"name":"Xue Li","is_ca":false},{"name":"Harry Campbell","is_ca":false},{"name":"Tim Varley","is_ca":false},{"name":"Juan Zhao","is_ca":false},{"name":"Robert J. Carroll","is_ca":false},{"name":"Lisa Bastarache","is_ca":false},{"name":"Joshua C. Denny","is_ca":false},{"name":"Evropi Τheodoratou","is_ca":false},{"name":"Wei‐Qi Wei","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1801527069750413,"gpt":0.473251173470247,"spread":0.2930984664952057,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1360246,0.001346415,0.000954684,0.007336924,0.001956799,0.005066393,0.003276115,0.0008491388,0.002724168],"category_scores_gemma":[0.3365164,0.0008748339,0.001834165,0.005607329,0.001149983,0.003531234,0.005764648,0.002153745,0.001524637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004776424,"about_ca_system_score_gemma":0.0200663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02442237,"about_ca_topic_score_gemma":0.01655793,"domain_scores_codex":[0.9175683,0.05015577,0.01016859,0.005940927,0.01482236,0.001344207],"domain_scores_gemma":[0.6941948,0.1494257,0.01515914,0.03810963,0.09898091,0.004129843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001729892,0.001437724,0.1583604,0.002735466,0.0004592302,0.0006963535,0.01894782,0.01831735,0.00549923,0.008378666,0.02779448,0.7556433],"study_design_scores_gemma":[0.002129054,0.003475556,0.3719802,0.006805893,0.0008985136,0.001964157,0.03792458,0.2840899,0.07046222,0.03867147,0.1806663,0.0009320601],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3153695,0.001379142,0.6089443,0.006051523,0.0005754916,0.02582991,0.01732679,0.01294578,0.01157752],"genre_scores_gemma":[0.2605907,0.0007722231,0.7091745,0.0005032138,0.00009128594,0.01153586,0.01471651,0.001297632,0.001318168],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1360246,"threshold_uncertainty_score":0.7193756,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2966351171","doi":"10.2196/12239","title":"Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review","year":2019,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":556,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"European Commission","keywords":"Complement (music); Health records; Data science; Electronic health record; Disease; Computer science; Medicine; Artificial intelligence; Domain (mathematical analysis); Natural language processing; Intensive care medicine; Health care; Pathology","authors":[{"name":"Seyedmostafa Sheikhalishahi","is_ca":false},{"name":"Riccardo Miotto","is_ca":false},{"name":"Joel T. Dudley","is_ca":false},{"name":"Alberto Lavelli","is_ca":false},{"name":"Fabio Rinaldi","is_ca":false},{"name":"Venet Osmani","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0602109193283869,"gpt":0.4754059535831966,"spread":0.4151950342548097,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01612939,0.001616093,0.006557775,0.01529192,0.0007340176,0.002496531,0.002535265,0.001741349,0.006988435],"category_scores_gemma":[0.09646965,0.0008719662,0.008387824,0.01333711,0.001331655,0.004382599,0.002044613,0.001373833,0.0006911996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003465377,"about_ca_system_score_gemma":0.01646394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007948767,"about_ca_topic_score_gemma":0.02075449,"domain_scores_codex":[0.9845583,0.005978066,0.005600593,0.001099976,0.002498734,0.0002643066],"domain_scores_gemma":[0.8524041,0.1229328,0.01508139,0.001915046,0.007237675,0.0004289057],"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":[0.0001689767,0.00001929759,0.0005855654,0.9439244,0.005958106,0.00008898548,0.0002469644,0.0001215226,0.00011161,0.0002133578,0.002065919,0.04649533],"study_design_scores_gemma":[0.0001748752,0.0001788627,0.003798386,0.928665,0.04159273,0.0003037408,0.0004369027,0.0001572806,0.0002805376,0.0005514031,0.02380745,0.00005280212],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001327801,0.9951491,0.0006162716,0.0005661814,0.0001579148,0.0005874297,0.001105807,0.00003008535,0.0004594142],"genre_scores_gemma":[0.01905156,0.9749095,0.002417192,0.001115579,0.0001611297,0.001234026,0.0009395567,0.00001788231,0.000153523],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01612939,"threshold_uncertainty_score":0.0853014,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2523834880","doi":"10.2196/medinform.5909","title":"Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach","year":2016,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":533,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Science Foundation","keywords":"Sepsis; Medicine; Systemic inflammatory response syndrome; Mews; Intensive care unit; Early warning score; Machine learning; Intensive care medicine; Electronic health record; Intensive care; Artificial intelligence; Medical record; Computer science; Emergency medicine; Health care; Internal medicine","authors":[{"name":"Thomas Desautels","is_ca":false},{"name":"Jacob Calvert","is_ca":false},{"name":"Jana Hoffman","is_ca":false},{"name":"Melissa Jay","is_ca":false},{"name":"Yaniv Kerem","is_ca":false},{"name":"Lisa Shieh","is_ca":false},{"name":"David Shimabukuro","is_ca":false},{"name":"Uli K. Chettipally","is_ca":false},{"name":"Mitchell D. Feldman","is_ca":false},{"name":"Chris W. Barton","is_ca":false},{"name":"David J. Wales","is_ca":false},{"name":"Ritankar Das","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09192372534928704,"gpt":0.3520840859675537,"spread":0.2601603606182666,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004149246,0.0007279536,0.0008425897,0.002223901,0.0002697185,0.0009974497,0.001011127,0.0007751424,0.0005439205],"category_scores_gemma":[0.01465429,0.0002464734,0.0006799378,0.001437076,0.0002325179,0.001001707,0.0008155532,0.0008612018,0.0002192915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005994287,"about_ca_system_score_gemma":0.00100845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004738177,"about_ca_topic_score_gemma":0.004128111,"domain_scores_codex":[0.998252,0.0007891427,0.0002068954,0.0004068554,0.0002542643,0.00009080686],"domain_scores_gemma":[0.9882426,0.00826236,0.001489968,0.000864268,0.0008497837,0.0002911828],"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":[0.002098638,0.001855938,0.6018141,0.0002684423,0.000477915,0.0003229508,0.0001434716,0.1473152,0.002644452,0.0006529556,0.003701743,0.2387042],"study_design_scores_gemma":[0.00008879104,0.0004840428,0.06843901,0.00004168304,0.00007342953,0.0001294799,0.00007405847,0.9277294,0.001168741,0.001224735,0.0005205742,0.00002602398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9281691,0.000827597,0.06262898,0.001143142,0.00006482098,0.000279542,0.005643445,0.0005091899,0.0007340653],"genre_scores_gemma":[0.9470385,0.0002647891,0.04440189,0.0001737069,0.0001298138,0.0001403369,0.007592762,0.000009894157,0.0002482122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004738177,"threshold_uncertainty_score":0.02194357,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3044626901","doi":"10.2196/18599","title":"Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review","year":2020,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":410,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Patient safety; Artificial intelligence; Systematic review; Clinical decision support system; Machine learning; MEDLINE; Decision tree; Computer science; Medicine; Decision support system; Health care","authors":[{"name":"Avishek Choudhury","is_ca":false},{"name":"Onur Asan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09932253929292394,"gpt":0.447337276712411,"spread":0.348014737419487,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02512966,0.001774128,0.007135579,0.01600002,0.0007549928,0.003784762,0.002493213,0.002328907,0.004650446],"category_scores_gemma":[0.1509219,0.001133807,0.009183514,0.01627067,0.001565123,0.00334343,0.002199873,0.001702621,0.0004083178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004830957,"about_ca_system_score_gemma":0.01807272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006690084,"about_ca_topic_score_gemma":0.01480469,"domain_scores_codex":[0.971962,0.01086524,0.01078947,0.001468975,0.004439292,0.0004749787],"domain_scores_gemma":[0.7830852,0.1784904,0.02491144,0.002165354,0.01055091,0.0007967093],"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":[0.0001418894,0.00001571189,0.001196194,0.9632957,0.007841702,0.00006221177,0.0002084955,0.0001108201,0.00004989221,0.0001817327,0.0009507839,0.02594499],"study_design_scores_gemma":[0.0001362419,0.0001409821,0.00309731,0.9420055,0.04685574,0.0001931272,0.0002573331,0.0001115815,0.0001105093,0.0003065931,0.006753929,0.00003110892],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001442243,0.9962681,0.0003572832,0.0005062787,0.0001268593,0.0004468264,0.000532804,0.00001273531,0.0003069482],"genre_scores_gemma":[0.03093122,0.9643955,0.001561325,0.001011151,0.0001636471,0.001373518,0.0004556317,0.0000121425,0.00009595897],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.02512966,"threshold_uncertainty_score":0.1328999,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2549346488","doi":"10.2196/medinform.5359","title":"Challenges and Opportunities of Big Data in Health Care: A Systematic Review","year":2016,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":405,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Big data; Health care; CINAHL; Data science; Analytics; Standardization; Data governance; Data quality; Computer science; Data management; MEDLINE; Medicine; Knowledge management; Business; Political science; Data mining; Marketing","authors":[{"name":"Clemens Scott Kruse","is_ca":false},{"name":"Rishi Goswamy","is_ca":false},{"name":"Yesha Raval","is_ca":false},{"name":"Sarah Marawi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.6379363503337271,"gpt":0.576138637263599,"spread":0.06179771307012805,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01328455,0.001133866,0.004061979,0.01196569,0.0009700772,0.003385757,0.00188513,0.002316663,0.002667449],"category_scores_gemma":[0.05152404,0.000899027,0.004370052,0.01580351,0.00110432,0.004633662,0.002229827,0.001577099,0.0003049431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003944178,"about_ca_system_score_gemma":0.02327818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006878088,"about_ca_topic_score_gemma":0.02416387,"domain_scores_codex":[0.9899055,0.003964592,0.002975617,0.0005107265,0.00232306,0.0003204548],"domain_scores_gemma":[0.9388221,0.04925437,0.005816023,0.000617407,0.004843516,0.0006465194],"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":[0.00007935485,0.00002141724,0.001002645,0.857328,0.002457874,0.0001741242,0.0007921741,0.0001557951,0.0001321911,0.001072379,0.004619669,0.1321645],"study_design_scores_gemma":[0.00004406881,0.00008873079,0.002033139,0.9336239,0.009422631,0.0004633695,0.001068094,0.0001014867,0.0001208553,0.0008302428,0.0521658,0.00003768499],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003087183,0.9984924,0.0001330646,0.0005947212,0.00009590069,0.00008303265,0.00006439204,0.000003053399,0.00022467],"genre_scores_gemma":[0.004024701,0.9946818,0.0005106633,0.0004496724,0.00006121046,0.00017056,0.00005273502,0.000002013947,0.00004679368],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01328455,"threshold_uncertainty_score":0.07025623,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3013605954","doi":"10.2196/17984","title":"Clinical Text Data in Machine Learning: Systematic Review","year":2020,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":367,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Medical Research Council","keywords":"Machine learning; Computer science; Artificial intelligence; Natural language processing; Bottleneck; Information retrieval","authors":[{"name":"‪Irena Spasić","is_ca":false},{"name":"Goran Nenadić","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4199594948799194,"gpt":0.57648193156965,"spread":0.1565224366897306,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05967357,0.00202138,0.01092943,0.02417236,0.001078597,0.004510407,0.004265131,0.003326547,0.007681015],"category_scores_gemma":[0.3079533,0.001481128,0.008695208,0.02310254,0.002923258,0.007302394,0.003296672,0.003313207,0.0008845891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005675223,"about_ca_system_score_gemma":0.02431548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005426588,"about_ca_topic_score_gemma":0.01162474,"domain_scores_codex":[0.9263734,0.0416485,0.01967826,0.003269091,0.008444362,0.0005863397],"domain_scores_gemma":[0.4928944,0.4584301,0.02875779,0.006347869,0.01266152,0.0009083095],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001058877,0.00001817831,0.0006199081,0.9334777,0.007581423,0.00007662541,0.0002100537,0.000327576,0.00005066896,0.0007061054,0.00347226,0.05335358],"study_design_scores_gemma":[0.0001619011,0.0001113812,0.001366155,0.9576055,0.01973278,0.0001575859,0.0002202327,0.0003660689,0.0001799262,0.002276975,0.01776934,0.00005216907],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005650302,0.9930172,0.002097965,0.001546089,0.0002800988,0.0009421079,0.001077667,0.00004913128,0.0004246494],"genre_scores_gemma":[0.02296181,0.9599071,0.008885507,0.002345235,0.0003974547,0.004025235,0.001278671,0.00004781226,0.0001511628],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9403265,"threshold_uncertainty_score":0.3155878,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963852062","doi":"10.2196/10010","title":"Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review","year":2019,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":365,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Medical diagnosis; CINAHL; MEDLINE; Scopus; Artificial intelligence; Medicine; Systematic review; Referral; Disease; Health care; Receiver operating characteristic; Applications of artificial intelligence; Cochrane Library; Inclusion and exclusion criteria; Health technology; Machine learning; Computer science; Meta-analysis; Family medicine; Alternative medicine; Pathology; Psychological intervention; Psychiatry","authors":[{"name":"Jiayi Shen","is_ca":false},{"name":"Casper J. P. Zhang","is_ca":false},{"name":"Bangsheng Jiang","is_ca":false},{"name":"Jiebin Chen","is_ca":false},{"name":"Jian Song","is_ca":false},{"name":"Zherui Liu","is_ca":false},{"name":"Zonglin He","is_ca":false},{"name":"Sum Yi Wong","is_ca":false},{"name":"Po-Han Fang","is_ca":false},{"name":"Wai‐Kit Ming","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3838209004827958,"gpt":0.5453718196699511,"spread":0.1615509191871553,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02315677,0.001897401,0.01096424,0.01534658,0.0006609818,0.00413824,0.002659003,0.002822159,0.005870234],"category_scores_gemma":[0.1194944,0.001204161,0.009439539,0.01586025,0.001553706,0.004016984,0.001982238,0.001749259,0.0004064009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006586286,"about_ca_system_score_gemma":0.01355525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007557652,"about_ca_topic_score_gemma":0.01814244,"domain_scores_codex":[0.9725928,0.009964488,0.01099465,0.001679381,0.004296789,0.0004718826],"domain_scores_gemma":[0.861576,0.1144528,0.01668399,0.001110724,0.005541768,0.000634753],"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":[0.0002005372,0.00000984927,0.0008592866,0.9656914,0.01068607,0.00005418277,0.000140998,0.00006621805,0.0000398053,0.0001888207,0.0007766666,0.02128621],"study_design_scores_gemma":[0.0002618732,0.0001463081,0.00241684,0.9249281,0.06377678,0.0002025006,0.0002210986,0.00009035735,0.00007625494,0.0003146813,0.007538973,0.00002626431],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005509825,0.9981579,0.0001501366,0.0002917783,0.0001369679,0.0003064332,0.0002114739,0.000005075066,0.0001892058],"genre_scores_gemma":[0.01557645,0.9810368,0.0008570645,0.0009413578,0.0002474152,0.001002729,0.0002339267,0.000006796067,0.00009754772],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.02315677,"threshold_uncertainty_score":0.1224662,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2799930623","doi":"10.2196/medinform.8912","title":"Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis","year":2018,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":357,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Clinical decision support system; Computer science; Adaptation (eye); Task (project management); Decision support system; Implementation; User requirements document; Knowledge management; Artificial intelligence; Software engineering; Systems engineering; Engineering; Psychology","authors":[{"name":"Saif Khairat","is_ca":false},{"name":"David T. Marc","is_ca":false},{"name":"William Crosby","is_ca":false},{"name":"Ali Al Sanousi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2051662262378013,"gpt":0.5933476856428958,"spread":0.3881814594050944,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2345424,0.001077627,0.001075502,0.01283688,0.006082933,0.009657412,0.003117869,0.003493272,0.002503625],"category_scores_gemma":[0.6018103,0.001149533,0.002458282,0.006208152,0.006018921,0.007917184,0.004342879,0.004975684,0.0003984181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02114099,"about_ca_system_score_gemma":0.03770557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005238203,"about_ca_topic_score_gemma":0.004798709,"domain_scores_codex":[0.7607306,0.1496781,0.03421048,0.006665284,0.04410135,0.004614187],"domain_scores_gemma":[0.1900261,0.6298725,0.05924083,0.007371213,0.1087458,0.004743573],"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":[0.001947195,0.0006031432,0.1245436,0.03050822,0.001117305,0.004233834,0.3125651,0.002262371,0.00323311,0.02620748,0.0606094,0.4321694],"study_design_scores_gemma":[0.0007066627,0.003012191,0.09679081,0.06616284,0.002468331,0.006640425,0.4983477,0.01449079,0.01208614,0.05860874,0.239169,0.001516393],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.5034754,0.05977758,0.05223055,0.3432665,0.004504144,0.01344845,0.001141309,0.0006772291,0.02147888],"genre_scores_gemma":[0.9145014,0.01116202,0.04584129,0.02190925,0.0009166143,0.003877527,0.0002991151,0.0001879072,0.001304988],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.2345424,"threshold_uncertainty_score":0.9439455,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3040316091","doi":"10.2196/20359","title":"Utilization Barriers and Medical Outcomes Commensurate With the Use of Telehealth Among Older Adults: Systematic Review","year":2020,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":283,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Telehealth; CINAHL; eHealth; Psychological intervention; Medicine; MEDLINE; mHealth; Telecare; Telemedicine; Systematic review; Quality of life (healthcare); Health care; Gerontology; Nursing","authors":[{"name":"Clemens Scott Kruse","is_ca":false},{"name":"Joanna Fohn","is_ca":false},{"name":"Nakia Wilson","is_ca":false},{"name":"Evangelina Núñez Patlán","is_ca":false},{"name":"Stephanie Zipp","is_ca":false},{"name":"Michael Mileski","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07198293453629405,"gpt":0.4006316719725751,"spread":0.328648737436281,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02120484,0.001632706,0.00844274,0.0113168,0.0009661226,0.00337633,0.001886851,0.00200903,0.002491577],"category_scores_gemma":[0.1147554,0.001681362,0.009494812,0.01293617,0.001193528,0.003382405,0.002044366,0.001234107,0.0001850298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005775067,"about_ca_system_score_gemma":0.0212023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01017258,"about_ca_topic_score_gemma":0.02553232,"domain_scores_codex":[0.9788899,0.006066158,0.009409282,0.001236003,0.003969588,0.0004291416],"domain_scores_gemma":[0.9000567,0.06540503,0.02469224,0.001187625,0.007887688,0.0007706202],"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":[0.0001444166,0.0000191588,0.002945488,0.9638783,0.01132081,0.00007547867,0.0005289624,0.0000615467,0.0000711419,0.0001218686,0.000834278,0.01999857],"study_design_scores_gemma":[0.0001833753,0.0002079854,0.0100811,0.9107378,0.06954863,0.0003110581,0.0008219425,0.00008218943,0.0002051674,0.0001902853,0.007583895,0.00004670796],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.006767648,0.988608,0.000452554,0.0005699281,0.0001650603,0.001894595,0.001006403,0.00002353458,0.0005122529],"genre_scores_gemma":[0.06591575,0.9251156,0.002287574,0.0009037907,0.000113377,0.004725467,0.0006743166,0.00001726148,0.0002467913],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.02120484,"threshold_uncertainty_score":0.1121433,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3172260675","doi":"10.2196/21929","title":"The Fast Health Interoperability Resources (FHIR) Standard: Systematic Literature Review of Implementations, Applications, Challenges and Opportunities","year":2021,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":280,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Interoperability; Computer science; Systematic review; Documentation; Health care; Health information technology; Health informatics; Implementation; Data science; Knowledge management; MEDLINE; Medicine; World Wide Web; Software engineering; Nursing; Public health","authors":[{"name":"Muhammad Ayaz","is_ca":false},{"name":"Muhammad Fermi Pasha","is_ca":false},{"name":"Mohammed Alzahrani","is_ca":false},{"name":"Rahmat Budiarto","is_ca":false},{"name":"Deris Stiawan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1448242637845235,"gpt":0.507788053023976,"spread":0.3629637892394525,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02936713,0.001689329,0.006428666,0.02860387,0.001291464,0.003604487,0.002772024,0.002802519,0.00437204],"category_scores_gemma":[0.1049346,0.001407224,0.007300316,0.02487578,0.001664672,0.005903598,0.003263188,0.001576081,0.0005117779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007222021,"about_ca_system_score_gemma":0.04343292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01174052,"about_ca_topic_score_gemma":0.03570225,"domain_scores_codex":[0.9727209,0.007925575,0.01190427,0.001431555,0.005349926,0.000667764],"domain_scores_gemma":[0.8869849,0.07715031,0.02043682,0.001762477,0.01261906,0.001046402],"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":[0.00007781636,0.00001644041,0.0007614105,0.9617053,0.001811806,0.0001159418,0.0005381753,0.00006899883,0.0001230838,0.0003717225,0.001861354,0.03254793],"study_design_scores_gemma":[0.00006225636,0.00008911113,0.001891651,0.973815,0.01093866,0.0002035336,0.000746706,0.00004924198,0.0001182908,0.0002554564,0.01180356,0.00002657645],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002318902,0.9926358,0.0006078569,0.001067524,0.0002319109,0.001378958,0.001101542,0.00002004604,0.00063755],"genre_scores_gemma":[0.01566732,0.9767398,0.002486842,0.001372352,0.0001080071,0.002648704,0.0007597804,0.00001306945,0.0002040865],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.02936713,"threshold_uncertainty_score":0.1553101,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2286621504","doi":"10.2196/medinform.4514","title":"The Impact of Information Technology on Patient Engagement and Health Behavior Change: A Systematic Review of the Literature","year":2016,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":273,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"PsycINFO; Usability; Psychological intervention; Inclusion (mineral); MEDLINE; Health care; Medicine; Health information technology; Behavior change; Medical education; Psychology; Applied psychology; Nursing; Computer science; Social psychology","authors":[{"name":"Suhila Sawesi","is_ca":false},{"name":"Mohamed Rashrash","is_ca":false},{"name":"Kanitha Phalakornkule","is_ca":false},{"name":"Janet S. Carpenter","is_ca":false},{"name":"Josette Jones","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07575827349535766,"gpt":0.4797367282505871,"spread":0.4039784547552294,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01845429,0.001457992,0.006834916,0.01728219,0.0009840801,0.004272983,0.002045093,0.002741585,0.004185105],"category_scores_gemma":[0.08439414,0.001214747,0.007752782,0.01916914,0.001243113,0.004275516,0.002120656,0.001768881,0.0003278277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005573925,"about_ca_system_score_gemma":0.02084254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008326111,"about_ca_topic_score_gemma":0.02277119,"domain_scores_codex":[0.980046,0.007549108,0.007437559,0.001038135,0.003497313,0.0004318429],"domain_scores_gemma":[0.8899092,0.08903895,0.01311596,0.0009486662,0.006225769,0.0007614612],"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":[0.0001294943,0.00003047969,0.001310145,0.9494095,0.004911819,0.0001002824,0.0004848986,0.00006600059,0.00006851473,0.0001772181,0.0009643016,0.04234736],"study_design_scores_gemma":[0.0001111574,0.0001747451,0.003885986,0.9596195,0.02642247,0.0002658508,0.0006138395,0.00007072638,0.0001094597,0.0001943312,0.008501805,0.00003015576],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001622942,0.9968339,0.0001429394,0.0003563961,0.0001025208,0.0003325217,0.0002632518,0.000006439901,0.0003391832],"genre_scores_gemma":[0.02060199,0.9768092,0.000765654,0.0006107001,0.0001020686,0.0008319732,0.0001870141,0.000005773644,0.00008564562],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01845429,"threshold_uncertainty_score":0.09759682,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2801958627","doi":"10.2196/medinform.8805","title":"Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation","year":2018,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":247,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institute of Biomedical Imaging and Bioengineering; National Human Genome Research Institute; National Research Foundation of Korea; National Research Foundation","keywords":"Homomorphic encryption; Computer science; Encryption; Logistic regression; Machine learning; Cloud computing; Discriminative model; Support vector machine; Computation; Data mining; Artificial intelligence; Outsourcing; Classifier (UML); Algorithm; Computer security","authors":[{"name":"Miran Kim","is_ca":false},{"name":"Yongsoo Song","is_ca":false},{"name":"Shuang Wang","is_ca":false},{"name":"Yuhou Xia","is_ca":false},{"name":"Xiaoqian Jiang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05255794342517325,"gpt":0.3334524473528274,"spread":0.2808945039276542,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003690063,0.000745469,0.0009143213,0.0006173601,0.0003214038,0.001260511,0.002199714,0.0009544839,0.0033338],"category_scores_gemma":[0.008720457,0.000372369,0.0004875373,0.0005829761,0.0007191679,0.002161703,0.001418484,0.001055962,0.001195311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001204084,"about_ca_system_score_gemma":0.001299493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001288918,"about_ca_topic_score_gemma":0.0006208787,"domain_scores_codex":[0.9970698,0.001109022,0.0001468405,0.0002964807,0.001133258,0.0002446282],"domain_scores_gemma":[0.9959037,0.001653158,0.0004394807,0.0008519134,0.0009442698,0.0002075144],"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":[0.003568576,0.001420534,0.007010076,0.0009230648,0.0003458593,0.0006904146,0.0001446015,0.5862992,0.02006319,0.01994442,0.006751638,0.3528385],"study_design_scores_gemma":[0.0001635482,0.0005643083,0.0003415363,0.00001328217,0.00002992487,0.0001949632,0.00001747004,0.9874207,0.008927232,0.001383894,0.0009284175,0.00001476955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1974242,0.002011867,0.7883007,0.001046921,0.0001374172,0.000958919,0.0002139346,0.00430473,0.005601278],"genre_scores_gemma":[0.8657269,0.0009397728,0.130449,0.0001491486,0.00003628685,0.0003240963,0.0002040047,0.0001383031,0.002032429],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003690063,"threshold_uncertainty_score":0.01951516,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285315819","doi":"10.2196/35724","title":"Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review","year":2022,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":233,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft","keywords":"Interoperability; Systematic review; Standardization; Health care; Computer science; MEDLINE; Data science; Health informatics; Medicine; World Wide Web; Public health; Knowledge management; Nursing; Political science","authors":[{"name":"Carina Nina Vorisek","is_ca":false},{"name":"Moritz Lehne","is_ca":false},{"name":"Sophie Anne Inès Klopfenstein","is_ca":false},{"name":"Paula Josephine Mayer","is_ca":false},{"name":"Alexander Bartschke","is_ca":false},{"name":"Thomas Haese","is_ca":false},{"name":"Sylvia Thun","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4284319628944909,"gpt":0.6008191873530828,"spread":0.1723872244585919,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05641013,0.002373044,0.01243245,0.03170263,0.001450191,0.005098522,0.003576956,0.003087582,0.009179831],"category_scores_gemma":[0.1996394,0.001804811,0.0138982,0.02929319,0.002394491,0.008869749,0.004912104,0.002181791,0.0007614713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009240881,"about_ca_system_score_gemma":0.03769223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008783135,"about_ca_topic_score_gemma":0.021528,"domain_scores_codex":[0.9344788,0.0252145,0.02565042,0.002650807,0.01098253,0.001022899],"domain_scores_gemma":[0.8054244,0.1382748,0.03637063,0.005408023,0.01336185,0.001160214],"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":[0.00009016763,0.00001216991,0.00069493,0.967129,0.006854796,0.00004310254,0.0002010438,0.00009483252,0.00006286557,0.0003444976,0.002065783,0.0224069],"study_design_scores_gemma":[0.0001959474,0.00007369834,0.002036925,0.9498808,0.03494368,0.0001157997,0.0002550718,0.0001195563,0.0001374367,0.0005549626,0.01163843,0.00004783324],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001280613,0.9880708,0.0016667,0.001509003,0.000323877,0.004561104,0.001777912,0.00008516987,0.000724808],"genre_scores_gemma":[0.02980566,0.9330229,0.01299968,0.002358572,0.0002511569,0.01939571,0.001708283,0.00004956654,0.0004084271],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.05641013,"threshold_uncertainty_score":0.2983289,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2998524378","doi":"10.2196/15182","title":"Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":233,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Duke Institute for Health Innovation; John D. and Catherine T. MacArthur Foundation","keywords":"Workflow; Health care; Artificial intelligence; Quality management; Multidisciplinary approach; Medicine; Clinical decision support system; Deep learning; Computer science; Knowledge management; Machine learning; Engineering; Decision support system; Management system; Operations management","authors":[{"name":"Mark Sendak","is_ca":false},{"name":"William Ratliff","is_ca":false},{"name":"Dina Sarro","is_ca":false},{"name":"Elizabeth Alderton","is_ca":false},{"name":"Joseph Futoma","is_ca":false},{"name":"Michael Gao","is_ca":false},{"name":"Marshall Nichols","is_ca":false},{"name":"Mike Revoir","is_ca":false},{"name":"Faraz Yashar","is_ca":false},{"name":"Corinne Miller","is_ca":false},{"name":"Kelly Kester","is_ca":false},{"name":"Sahil Sandhu","is_ca":false},{"name":"Kristin Corey","is_ca":false},{"name":"Nathan Brajer","is_ca":false},{"name":"Christelle Tan","is_ca":false},{"name":"Anthony Lin","is_ca":false},{"name":"Tres Brown","is_ca":false},{"name":"Susan Engelbosch","is_ca":false},{"name":"Kevin J. Anstrom","is_ca":false},{"name":"Madeleine Clare Elish","is_ca":false},{"name":"Katherine Heller","is_ca":false},{"name":"Rebecca Donohoe","is_ca":false},{"name":"Jason Theiling","is_ca":false},{"name":"Eric G. Poon","is_ca":false},{"name":"Suresh Balu","is_ca":false},{"name":"Armando Bedoya","is_ca":false},{"name":"Cara O’Brien","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05064018169356722,"gpt":0.4546535408726954,"spread":0.4040133591791282,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04001194,0.0007769291,0.0005259677,0.001398769,0.001099861,0.003335959,0.001986722,0.001533759,0.002055943],"category_scores_gemma":[0.0592872,0.0008416004,0.0009904408,0.001140489,0.001534794,0.003393464,0.005335514,0.00298131,0.0007187818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00641159,"about_ca_system_score_gemma":0.01025991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006606822,"about_ca_topic_score_gemma":0.006914187,"domain_scores_codex":[0.9758538,0.01391724,0.001731171,0.001639164,0.003946082,0.002912626],"domain_scores_gemma":[0.9517981,0.01593322,0.00492245,0.00581628,0.01254528,0.008984643],"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":[0.005265147,0.06347954,0.2780002,0.001555725,0.0007338207,0.0008060174,0.0086498,0.02009655,0.005242242,0.003642784,0.01594089,0.5965873],"study_design_scores_gemma":[0.007785466,0.1530335,0.5836912,0.002977735,0.0011795,0.001309556,0.02816109,0.1268135,0.02549021,0.00553857,0.06322577,0.0007938626],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977813,0.0002906329,0.01185921,0.002398872,0.0001192124,0.003240835,0.0005915841,0.0006095446,0.003077105],"genre_scores_gemma":[0.9674518,0.0003649648,0.02651065,0.001377495,0.00006876464,0.002303758,0.001024997,0.00008908588,0.0008085825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04001194,"threshold_uncertainty_score":0.2116059,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2982280746","doi":"10.2196/16023","title":"Sentiment Analysis in Health and Well-Being: Systematic Review","year":2019,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":218,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Cardiff University","keywords":"Computer science; Data science","authors":[{"name":"Anastazia Žunić","is_ca":false},{"name":"Padraig Corcoran","is_ca":false},{"name":"‪Irena Spasić","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03407258699547368,"gpt":0.3668961208757042,"spread":0.3328235338802305,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00927234,0.001472065,0.006034391,0.01052763,0.0007297547,0.00300656,0.001426794,0.001695421,0.005916917],"category_scores_gemma":[0.04723576,0.0006482071,0.006667694,0.008968943,0.001163105,0.002888686,0.001587892,0.001108091,0.0004751087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002752146,"about_ca_system_score_gemma":0.007869775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003900647,"about_ca_topic_score_gemma":0.01170626,"domain_scores_codex":[0.9925181,0.003038982,0.002400769,0.0005838925,0.001276512,0.0001817019],"domain_scores_gemma":[0.9485165,0.04086555,0.006682913,0.0004740832,0.003154814,0.000306143],"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":[0.0001603416,0.00002080992,0.001077883,0.9185397,0.004120425,0.00007905327,0.000383323,0.00008216815,0.0001282586,0.0003215069,0.003278649,0.07180792],"study_design_scores_gemma":[0.0001379746,0.0001757613,0.006307575,0.9321696,0.02913004,0.0003195213,0.0007181702,0.0001506259,0.0001535351,0.0007105171,0.02997692,0.00004993761],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0009937563,0.9971024,0.000199282,0.0004276841,0.0001634582,0.000299527,0.0004348873,0.000008904204,0.0003700214],"genre_scores_gemma":[0.01732807,0.9798068,0.000871949,0.0006583084,0.0001846175,0.0007247818,0.0002926105,0.000008517932,0.0001244395],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01052763,"threshold_uncertainty_score":0.0490374,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3118996476","doi":"10.2196/24207","title":"Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":211,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Center for Advancing Translational Sciences","keywords":"Machine learning; Artificial intelligence; Lasso (programming language); Computer science; Logistic regression; Generalizability theory; Health records; Multilayer perceptron; Predictive modelling; Deep learning; Electronic health record; Health care; Artificial neural network; Statistics","authors":[{"name":"Akhil Vaid","is_ca":false},{"name":"Suraj K. Jaladanki","is_ca":false},{"name":"Jie Xu","is_ca":false},{"name":"Shelly Teng","is_ca":false},{"name":"Arvind Kumar","is_ca":false},{"name":"Samuel Lee","is_ca":false},{"name":"Sulaiman Somani","is_ca":false},{"name":"Ishan Paranjpe","is_ca":false},{"name":"Jessica K. De Freitas","is_ca":false},{"name":"Tingyi Wanyan","is_ca":false},{"name":"Kipp W. Johnson","is_ca":false},{"name":"Mesude Bicak","is_ca":false},{"name":"Eyal Klang","is_ca":false},{"name":"Young Joon Kwon","is_ca":false},{"name":"Anthony Costa","is_ca":false},{"name":"Shan Zhao","is_ca":false},{"name":"Riccardo Miotto","is_ca":false},{"name":"Alexander W. Charney","is_ca":false},{"name":"Erwin P. Böttinger","is_ca":false},{"name":"Zahi A. Fayad","is_ca":false},{"name":"Girish N. Nadkarni","is_ca":false},{"name":"Fei Wang","is_ca":false},{"name":"Benjamin S. Glicksberg","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01285349720121259,"gpt":0.3090294637525138,"spread":0.2961759665513012,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009975429,0.00112327,0.001765832,0.001910781,0.0006534246,0.00165597,0.002188534,0.001398175,0.0009484857],"category_scores_gemma":[0.01842277,0.0005681149,0.001534441,0.001549347,0.0007953782,0.002729908,0.002421745,0.002164526,0.000291099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001559757,"about_ca_system_score_gemma":0.002249219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007901653,"about_ca_topic_score_gemma":0.007147268,"domain_scores_codex":[0.9964764,0.001769449,0.0002571151,0.0008400094,0.000396703,0.0002603421],"domain_scores_gemma":[0.9917763,0.004575562,0.0009628914,0.00112743,0.001282554,0.0002752032],"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":[0.0005672034,0.000995659,0.04656412,0.0001009566,0.0006253457,0.0002880902,0.0001958041,0.7470358,0.00110518,0.002890852,0.002233036,0.197398],"study_design_scores_gemma":[0.00001449332,0.00009212828,0.001582205,0.00001379895,0.00003074064,0.00002904729,0.00002248978,0.9946473,0.000404955,0.002944762,0.0002082717,0.000009774254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2537083,0.001006116,0.737965,0.003035414,0.0001330668,0.0002317478,0.0006649381,0.001982919,0.001272411],"genre_scores_gemma":[0.905606,0.0003012776,0.09161519,0.0004224044,0.0001253299,0.0001270485,0.0007101697,0.00003560032,0.001056884],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009975429,"threshold_uncertainty_score":0.05275577,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2966705814","doi":"10.2196/13042","title":"Electronic Consultation in Primary Care Between Providers and Patients: Systematic Review","year":2019,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Healthcare Systems and Technology","field":"Business, Management and Accounting","cited_by":208,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Primary care; Medicine; Telehealth; Systematic review; Medical emergency; MEDLINE; Telemedicine; Family medicine; Health care","authors":[{"name":"Freda Mold","is_ca":false},{"name":"Jane Hendy","is_ca":false},{"name":"Yi‐Ling Lai","is_ca":false},{"name":"Simon de Lusignan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02154616885431484,"gpt":0.3067114982341452,"spread":0.2851653293798304,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01171395,0.001098037,0.00635857,0.0058662,0.0008530083,0.00314699,0.00199472,0.002405287,0.008583476],"category_scores_gemma":[0.07851624,0.000902727,0.005259772,0.009474389,0.001090292,0.003178177,0.002060881,0.001721865,0.0004239591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006161658,"about_ca_system_score_gemma":0.01752033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008606934,"about_ca_topic_score_gemma":0.02101905,"domain_scores_codex":[0.9796708,0.01018363,0.005525898,0.0009979559,0.003151109,0.0004705563],"domain_scores_gemma":[0.9160473,0.06817765,0.01042115,0.000836589,0.003850393,0.0006668254],"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":[0.0001863742,0.00003298502,0.0004295292,0.9655201,0.002822137,0.0000854099,0.000386266,0.0000502904,0.00003308215,0.000136119,0.001361522,0.02895613],"study_design_scores_gemma":[0.0002827657,0.0002641872,0.002354721,0.9652796,0.01739086,0.000345136,0.0005870645,0.00005093845,0.00008518521,0.0001464536,0.01318755,0.0000254081],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001316303,0.9969108,0.0001116015,0.0002867412,0.0001054457,0.0005394956,0.0002608069,0.000008077936,0.0004606789],"genre_scores_gemma":[0.02553282,0.9712955,0.0007326524,0.0008091276,0.0001035938,0.001177856,0.0001917707,0.000005786673,0.0001509021],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01171395,"threshold_uncertainty_score":0.06195003,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3035916362","doi":"10.2196/19866","title":"The Role of Health Technology and Informatics in a Global Public Health Emergency: Practices and Implications From the COVID-19 Pandemic","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":198,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Pandemic; Telemedicine; Health care; Medicine; Public health; Telehealth; Coronavirus disease 2019 (COVID-19); Middle East respiratory syndrome; Digital health; Global health; Health informatics; Medical emergency; Business; Disease; Economic growth; Nursing; Infectious disease (medical specialty)","authors":[{"name":"Jiancheng Ye","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.089719897220137,"gpt":0.4288465134815742,"spread":0.3391266162614371,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01883991,0.0004752337,0.0003100927,0.004071437,0.003387807,0.01361075,0.001488817,0.004030232,0.004814418],"category_scores_gemma":[0.03698462,0.0003307731,0.0004734177,0.00337732,0.01364865,0.01198043,0.007645361,0.005507884,0.0008565546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005589013,"about_ca_system_score_gemma":0.008749957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005208958,"about_ca_topic_score_gemma":0.005005843,"domain_scores_codex":[0.9820402,0.0133388,0.000646783,0.0006630443,0.002329392,0.0009817249],"domain_scores_gemma":[0.9530069,0.03205043,0.002605768,0.002719791,0.004538687,0.005078303],"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":[0.0001325799,0.0004086518,0.0476601,0.002110163,0.00007536783,0.001875733,0.06321859,0.0008478621,0.0006542967,0.2296493,0.1028499,0.5505175],"study_design_scores_gemma":[0.00005858273,0.0003518677,0.03090556,0.01046204,0.00007919247,0.003189067,0.1853498,0.00172924,0.001034511,0.1391984,0.6275021,0.0001396687],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.03172045,0.04018951,0.004370292,0.8232689,0.001729225,0.00007488844,0.00008230583,0.0001480542,0.09841634],"genre_scores_gemma":[0.7834429,0.1103338,0.01278602,0.08147167,0.004077162,0.0001622562,0.0001024051,0.0001484571,0.00747531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01883991,"threshold_uncertainty_score":0.09963614,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2797870998","doi":"10.2196/medinform.7744","title":"Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis","year":2018,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":191,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; U.S. National Library of Medicine; National Institute of General Medical Sciences; National Human Genome Research Institute; National Heart, Lung, and Blood Institute; National Science Foundation","keywords":"Homomorphic encryption; Computer science; Hash function; Similarity (geometry); Encryption; Context (archaeology); Matching (statistics); Data mining; Information retrieval; Computer security; Artificial intelligence; Medicine","authors":[{"name":"Junghye Lee","is_ca":false},{"name":"Jimeng Sun","is_ca":false},{"name":"Fei Wang","is_ca":false},{"name":"Shuang Wang","is_ca":false},{"name":"Chi‐Hyuck Jun","is_ca":false},{"name":"Xiaoqian Jiang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01643724248623696,"gpt":0.2847583559313596,"spread":0.2683211134451227,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008773264,0.0004574598,0.001056595,0.0009978837,0.0005816561,0.002012982,0.001869919,0.001197673,0.001012405],"category_scores_gemma":[0.01477082,0.0003575016,0.001192986,0.001166018,0.001131924,0.003980461,0.002786292,0.001723579,0.0003184021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001716508,"about_ca_system_score_gemma":0.003053291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002152441,"about_ca_topic_score_gemma":0.001012871,"domain_scores_codex":[0.994672,0.002021345,0.0003901907,0.0009563763,0.00159742,0.0003626846],"domain_scores_gemma":[0.9896012,0.004251904,0.0008289352,0.002653399,0.002179451,0.0004849882],"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":[0.0005439203,0.0006349432,0.01580177,0.0001855508,0.0002520213,0.0004584455,0.0003290506,0.5778906,0.006369248,0.06421291,0.002912461,0.330409],"study_design_scores_gemma":[0.0000121828,0.00007898943,0.0005406018,0.00001109186,0.00001082841,0.00008902407,0.00003657836,0.9859746,0.001628636,0.010852,0.0007576394,0.000007725105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04219249,0.0004785815,0.9546021,0.0008617208,0.00004265548,0.0001857599,0.0001275059,0.0008241722,0.0006850087],"genre_scores_gemma":[0.633498,0.000587646,0.3643365,0.0002663054,0.00006453958,0.0001738701,0.0003397404,0.00003977954,0.0006936422],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008773264,"threshold_uncertainty_score":0.04639804,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4220718380","doi":"10.2196/36388","title":"Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":184,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Scopus; Machine learning; Artificial intelligence; MEDLINE; Computer science; Systematic review; Publication bias; Meta-analysis; Selection bias; Medicine; Data science; Pathology","authors":[{"name":"Jonathan Huang","is_ca":false},{"name":"Galal Galal","is_ca":false},{"name":"Mozziyar Etemadi","is_ca":false},{"name":"Mahesh Vaidyanathan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4425374768641928,"gpt":0.5513769212540904,"spread":0.1088394443898976,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1740836,0.002821157,0.009329785,0.02548906,0.002241619,0.009555576,0.006114386,0.005910623,0.00557113],"category_scores_gemma":[0.5604152,0.00215449,0.01530262,0.0175265,0.00409059,0.01030315,0.006263969,0.004367831,0.0007275742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01101726,"about_ca_system_score_gemma":0.05222614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01046341,"about_ca_topic_score_gemma":0.01644452,"domain_scores_codex":[0.8401977,0.0841516,0.05043268,0.005004935,0.01889097,0.001322186],"domain_scores_gemma":[0.383377,0.5262843,0.04053197,0.01253924,0.03617174,0.001095722],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0002486462,0.00005022033,0.001734536,0.8089471,0.009671843,0.0001057487,0.001224471,0.001184883,0.0001386268,0.004793129,0.003712743,0.1681881],"study_design_scores_gemma":[0.00005527215,0.00007280015,0.0005818813,0.9691348,0.01357039,0.00009113541,0.0003255135,0.0004112207,0.000197451,0.002264077,0.01326216,0.00003333577],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001232341,0.9854587,0.005596429,0.003092458,0.0006833425,0.001869264,0.000508633,0.00005238465,0.001506378],"genre_scores_gemma":[0.03524082,0.9389647,0.01651627,0.002380657,0.0006498527,0.005267008,0.0006462671,0.00006562215,0.0002687539],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8259164,"threshold_uncertainty_score":0.9206529,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2972483465","doi":"10.2196/14830","title":"Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)–Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":175,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; U.S. Department of Veterans Affairs","keywords":"Normalization (sociology); Computer science; Natural language processing; Named-entity recognition; Artificial intelligence; SNOMED CT; Transformer; Encoder; Language model; Information retrieval; Health records; Electronic health record; Machine learning; Task (project management); Health care; Linguistics; Terminology","authors":[{"name":"Fei Li","is_ca":false},{"name":"Yonghao Jin","is_ca":false},{"name":"Weisong Liu","is_ca":false},{"name":"Bhanu Pratap Singh Rawat","is_ca":false},{"name":"Pengshan Cai","is_ca":false},{"name":"Hong Yu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03612909331573435,"gpt":0.3803868410582724,"spread":0.3442577477425381,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006135539,0.002036104,0.0009786861,0.001442901,0.0006229141,0.001360017,0.002141243,0.001391824,0.002723341],"category_scores_gemma":[0.02103714,0.0007151957,0.001151065,0.001624218,0.0006774082,0.003833461,0.001497114,0.002917629,0.001676586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003049981,"about_ca_system_score_gemma":0.002539148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04063042,"about_ca_topic_score_gemma":0.04292474,"domain_scores_codex":[0.9974777,0.001319501,0.0001732545,0.0006019998,0.0002665148,0.0001611176],"domain_scores_gemma":[0.9824448,0.01392921,0.0004914187,0.001347414,0.001531244,0.0002558592],"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":[0.001133329,0.001298543,0.01860538,0.0008122419,0.0004886114,0.0003209896,0.0004355569,0.5046804,0.005125763,0.002445493,0.02706958,0.4375841],"study_design_scores_gemma":[0.00008895111,0.0002043761,0.002358831,0.00005288435,0.0001082672,0.00008704392,0.0001229955,0.9890055,0.004182559,0.00155695,0.002193567,0.00003801191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8049118,0.006765442,0.1536925,0.001830152,0.000609474,0.0006991973,0.008461047,0.01518419,0.007846131],"genre_scores_gemma":[0.8907543,0.001118706,0.08198144,0.0004997967,0.0001179494,0.0003920877,0.02019482,0.0005458899,0.004394924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04063042,"threshold_uncertainty_score":0.0807879,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2906029872","doi":"10.2196/11949","title":"Using Blockchain Technology to Manage Clinical Trials Data: A Proof-of-Concept Study","year":2018,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":174,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false},"ca_institutions":"Queen's University","funders":"Southeastern Ontario Academic Medical Organization","keywords":"Blockchain; Proof of concept; Computer science; Clinical trial; Data management; Data science; Process management; Medicine; Data mining; Computer security; Business; Pathology","authors":[{"name":"David M. Maslove","is_ca":true},{"name":"Jacob Klein","is_ca":true},{"name":"M. Kathryn Brohman","is_ca":true},{"name":"Patrick Martin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1397321832481064,"gpt":0.4593035411746271,"spread":0.3195713579265207,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03118295,0.0009857494,0.0007163696,0.0007803738,0.0007952088,0.002265095,0.001714858,0.002369631,0.006495709],"category_scores_gemma":[0.02389778,0.0004103305,0.001106957,0.0004736468,0.001603198,0.003171712,0.001726603,0.001746189,0.0008091431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001284291,"about_ca_system_score_gemma":0.005650671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006657626,"about_ca_topic_score_gemma":0.0004632558,"domain_scores_codex":[0.9846255,0.009982822,0.0004450589,0.0005556805,0.003590425,0.0008005355],"domain_scores_gemma":[0.9684339,0.02180797,0.00238863,0.001738112,0.004023104,0.001608363],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.02195241,0.02983265,0.0165695,0.02137786,0.001802038,0.003843652,0.004449017,0.03439487,0.1998381,0.08329991,0.02195432,0.5606857],"study_design_scores_gemma":[0.02749778,0.2145318,0.009299709,0.006151617,0.002005699,0.004722196,0.002737257,0.09517716,0.3791433,0.02974211,0.2285442,0.0004473015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5334071,0.01110452,0.3678416,0.006845901,0.001307023,0.04558967,0.001653912,0.0009745142,0.03127574],"genre_scores_gemma":[0.6783782,0.005718436,0.2938114,0.001446385,0.0001866324,0.01486912,0.0006045102,0.0001000295,0.00488528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9688171,"threshold_uncertainty_score":0.1649131,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2791458756","doi":"10.2196/medinform.8960","title":"Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis","year":2018,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":174,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institute of Environmental Health Sciences; National Institute of Allergy and Infectious Diseases; National Human Genome Research Institute; University of Pennsylvania; National Institutes of Health; Pennsylvania Department of Health","keywords":"Missing data; Imputation (statistics); Health records; Computer science; Data science; Electronic health record; Data mining; Health care; Machine learning","authors":[{"name":"Brett K. Beaulieu‐Jones","is_ca":false},{"name":"Daniel R. Lavage","is_ca":false},{"name":"John W Snyder","is_ca":false},{"name":"Jason H. Moore","is_ca":false},{"name":"Sarah A. Pendergrass","is_ca":false},{"name":"Christopher R. Bauer","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08677746776859628,"gpt":0.4334871571936644,"spread":0.3467096894250681,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1618055,0.001556426,0.002786629,0.006906434,0.002669608,0.005935527,0.006235376,0.002975062,0.001615854],"category_scores_gemma":[0.3850677,0.001786325,0.003955049,0.01226353,0.003473086,0.008335262,0.005702194,0.005043277,0.0007409051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002817116,"about_ca_system_score_gemma":0.009961076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003691247,"about_ca_topic_score_gemma":0.003521664,"domain_scores_codex":[0.7957572,0.1683882,0.01341859,0.006478245,0.01477504,0.001182708],"domain_scores_gemma":[0.4848964,0.4240991,0.03891189,0.03155181,0.01918129,0.001359564],"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":[0.0004572458,0.0006800584,0.1853109,0.009158053,0.003380133,0.0006694454,0.007679041,0.1260181,0.002272693,0.07612578,0.02304954,0.565199],"study_design_scores_gemma":[0.0003459515,0.00088503,0.04770602,0.009884449,0.001261775,0.001473706,0.006127028,0.439486,0.01023312,0.441892,0.0400249,0.0006798966],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01498847,0.002085174,0.9748694,0.004452876,0.0001264079,0.0009405634,0.0012791,0.0006104618,0.0006476479],"genre_scores_gemma":[0.1216429,0.002163111,0.8708085,0.0009797481,0.0002506851,0.001648132,0.002115493,0.0001677112,0.0002236778],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1618055,"threshold_uncertainty_score":0.8557194,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2418503617","doi":"10.2196/medinform.5525","title":"Adoption Factors of the Electronic Health Record: A Systematic Review","year":2016,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":165,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Texas State University","keywords":"CINAHL; Facilitator; Health information technology; MEDLINE; Clinical decision support system; Electronic health record; Health care; Medicine; Health information exchange; Nursing; Health informatics; Cochrane Library; Family medicine; Medical education; Psychological intervention; Psychology; Health information; Political science; Public health","authors":[{"name":"Clemens Scott Kruse","is_ca":false},{"name":"Krysta Kothman","is_ca":false},{"name":"Keshia Anerobi","is_ca":false},{"name":"Lillian Abanaka","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0782695182775771,"gpt":0.4817229635460851,"spread":0.403453445268508,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02068163,0.001275736,0.006678978,0.01518284,0.001143393,0.00321647,0.001887958,0.001772883,0.002427999],"category_scores_gemma":[0.08465148,0.001215647,0.007158365,0.0192367,0.001293892,0.004389462,0.001711692,0.001226691,0.0002259606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005870181,"about_ca_system_score_gemma":0.02303359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008607992,"about_ca_topic_score_gemma":0.02207969,"domain_scores_codex":[0.9758859,0.00767822,0.00830932,0.001422344,0.006263348,0.0004408735],"domain_scores_gemma":[0.8986968,0.07158492,0.01754281,0.001105362,0.01035656,0.0007135505],"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":[0.0001926708,0.00005662011,0.005188628,0.926565,0.006355819,0.0001843636,0.0008977855,0.00008503828,0.0001327384,0.000232229,0.0009318077,0.05917734],"study_design_scores_gemma":[0.0002156133,0.000372355,0.0163795,0.9278724,0.04171145,0.0006710289,0.001604063,0.0001228358,0.0002250786,0.000178336,0.01059096,0.00005639606],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.006833117,0.9901695,0.0003448336,0.0003612818,0.000105498,0.001239054,0.0004841084,0.00001060095,0.0004519939],"genre_scores_gemma":[0.04467225,0.95054,0.001999965,0.000389306,0.00006206931,0.001906949,0.0002903987,0.000006803851,0.0001321621],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.02068163,"threshold_uncertainty_score":0.1093763,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3033682799","doi":"10.2196/18910","title":"Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":164,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Horizon 2020 Framework Programme; European Commission","keywords":"Synthetic data; Machine learning; Computer science; Artificial intelligence; Random forest; Decision tree; Support vector machine; Generalizability theory; Data mining; Statistics; Mathematics","authors":[{"name":"Debbie Rankin","is_ca":false},{"name":"Michaela Black","is_ca":false},{"name":"Raymond Bond","is_ca":false},{"name":"Jonathan Wallace","is_ca":false},{"name":"Maurice Mulvenna","is_ca":false},{"name":"Gorka Epelde","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.152395735813922,"gpt":0.379612199091731,"spread":0.227216463277809,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02823386,0.0006905115,0.0009824767,0.0009083957,0.0005710328,0.001532384,0.001778458,0.001354033,0.0008444533],"category_scores_gemma":[0.08468535,0.000401442,0.0008568791,0.0007747887,0.001934636,0.002430261,0.00148459,0.001981408,0.0002582919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002168765,"about_ca_system_score_gemma":0.001610875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004339682,"about_ca_topic_score_gemma":0.002476961,"domain_scores_codex":[0.986833,0.0094915,0.0005453914,0.001429745,0.001329308,0.0003710354],"domain_scores_gemma":[0.9002564,0.07377248,0.007463906,0.01025585,0.007538681,0.000712678],"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":[0.000600511,0.0002285244,0.02303099,0.0001359263,0.0001386014,0.0000938068,0.0002801369,0.9254795,0.000847109,0.007776391,0.001347109,0.04004135],"study_design_scores_gemma":[0.00001197932,0.00008421523,0.001392686,0.00001743194,0.000009631389,0.0000332317,0.00003051476,0.9921203,0.0007086743,0.005362709,0.0002197737,0.000008955653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5552764,0.000816383,0.4364639,0.002711921,0.0001419501,0.0004223186,0.0007307637,0.0006011262,0.002835226],"genre_scores_gemma":[0.9716732,0.00009203424,0.02713172,0.000132436,0.00003173985,0.000128224,0.0004248637,0.00002221808,0.0003635988],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02823386,"threshold_uncertainty_score":0.1493167,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4394579747","doi":"10.2196/55318","title":"An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":162,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; U.S. National Library of Medicine; National Institutes of Health","keywords":"Computer science; Natural language processing; Artificial intelligence; Heuristic; Context (archaeology); Task (project management); Relationship extraction; Machine learning; Information extraction","authors":[{"name":"Sonish Sivarajkumar","is_ca":false},{"name":"Mark Kelley","is_ca":false},{"name":"Alyssa Samolyk-Mazzanti","is_ca":false},{"name":"Shyam Visweswaran","is_ca":false},{"name":"Yanshan Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2913679776604406,"gpt":0.5726627397999422,"spread":0.2812947621395016,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02173532,0.00199798,0.001557944,0.001186095,0.0006535358,0.001488542,0.002220594,0.002271382,0.002024983],"category_scores_gemma":[0.0621652,0.0008583414,0.001174816,0.0008503429,0.0009384629,0.002727245,0.002249957,0.003797875,0.0007696046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002242781,"about_ca_system_score_gemma":0.003017307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005626713,"about_ca_topic_score_gemma":0.005497971,"domain_scores_codex":[0.9931878,0.004395889,0.0005053881,0.001163237,0.0005380362,0.0002096171],"domain_scores_gemma":[0.9175776,0.07296889,0.001353819,0.002900098,0.004379707,0.0008198203],"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":[0.003807,0.003255352,0.015639,0.001710731,0.0006943344,0.0002946357,0.001008136,0.5364739,0.006905041,0.003814475,0.008165665,0.4182318],"study_design_scores_gemma":[0.00025115,0.0006988326,0.00141277,0.00005980899,0.0001089721,0.00008022798,0.0001418519,0.9909633,0.003779464,0.001652586,0.0008227713,0.00002831815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5592039,0.005422083,0.4165815,0.00116401,0.0003742436,0.002041454,0.001347743,0.01069498,0.003170098],"genre_scores_gemma":[0.715293,0.0007514203,0.2784394,0.000397218,0.0000577765,0.001175803,0.002628023,0.0002754665,0.0009817702],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02173532,"threshold_uncertainty_score":0.1149487,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2765292139","doi":"10.2196/medinform.8092","title":"Patient Portal Use and Experience Among Older Adults: Systematic Review","year":2017,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":159,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Agency for Healthcare Research and Quality; University of Washington; National Science Foundation","keywords":"Patient portal; Inclusion (mineral); Thematic analysis; Medicine; Population; Health care; Focus group; The Internet; MEDLINE; Gerontology; Systematic review; Qualitative research; Psychology; Medical education; World Wide Web; Computer science","authors":[{"name":"Dawn K. Sakaguchi-Tang","is_ca":false},{"name":"Alyssa Bosold","is_ca":false},{"name":"Yong Kyung Choi","is_ca":false},{"name":"Anne M. Turner","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03896772021135633,"gpt":0.4241418357765376,"spread":0.3851741155651813,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01258222,0.001187768,0.006070248,0.009760104,0.0007955555,0.003119983,0.001637746,0.001893452,0.003722687],"category_scores_gemma":[0.06377607,0.001032859,0.006751478,0.01333109,0.0009374039,0.003557874,0.001859161,0.001215355,0.0002681078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003768134,"about_ca_system_score_gemma":0.01195651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006043024,"about_ca_topic_score_gemma":0.01781411,"domain_scores_codex":[0.9868767,0.004140189,0.005535515,0.0008939479,0.002265802,0.0002877711],"domain_scores_gemma":[0.9367559,0.04358052,0.01407157,0.0007503689,0.00422941,0.0006122283],"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":[0.0001153524,0.00001523971,0.001301737,0.9729468,0.005726145,0.0001008347,0.0005489979,0.00003728012,0.00007125571,0.0001030032,0.0008277983,0.01820562],"study_design_scores_gemma":[0.0001589867,0.0002368075,0.005961963,0.9306934,0.04663239,0.0004938238,0.0009641425,0.00004933841,0.0001466416,0.0002116101,0.01441466,0.00003612561],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003230478,0.9947405,0.0001816717,0.0002887959,0.00008740585,0.0005365579,0.0005525221,0.000009845299,0.0003722685],"genre_scores_gemma":[0.03287518,0.9635509,0.000959597,0.0006788642,0.00008267523,0.001324189,0.0003622459,0.000007651821,0.0001586295],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01258222,"threshold_uncertainty_score":0.06654191,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2115683309","doi":"10.2196/medinform.2913","title":"Big Data and Clinicians: A Review on the State of the Science","year":2014,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":158,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Big data; Data science; Computer science; Pace; Data quality; Data mining","authors":[{"name":"Weiqi Wang","is_ca":false},{"name":"Eswar Krishnan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.5332844092986461,"gpt":0.6082372109656298,"spread":0.0749528016669837,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00485103,0.001063867,0.002368091,0.007757949,0.0007863756,0.002812831,0.001669966,0.00281209,0.004042963],"category_scores_gemma":[0.01227316,0.0005916117,0.001654082,0.01201789,0.0015816,0.004387599,0.001619741,0.002956837,0.001167128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002197523,"about_ca_system_score_gemma":0.006963077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00302155,"about_ca_topic_score_gemma":0.005095158,"domain_scores_codex":[0.9970846,0.0008975797,0.0006818606,0.0002575066,0.00094601,0.0001324178],"domain_scores_gemma":[0.9735336,0.02189378,0.001297021,0.0002901067,0.002561366,0.0004241513],"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":[0.00007041196,0.00005215579,0.0006540801,0.09328862,0.0002482475,0.0002419545,0.0004676979,0.0003086961,0.0002791684,0.006737975,0.04807249,0.8495785],"study_design_scores_gemma":[0.00002137969,0.00008255148,0.002185551,0.1078745,0.0004330795,0.001291317,0.0005455973,0.0001574742,0.0001930154,0.005985446,0.8811746,0.00005540151],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00004272452,0.997987,0.000089016,0.001183366,0.0002403826,0.00000627345,0.00001355679,0.000003749866,0.0004338858],"genre_scores_gemma":[0.000349876,0.9987113,0.0001699575,0.0004562412,0.0002363037,0.000009352788,0.00001285762,0.000001403789,0.00005264208],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.007757949,"threshold_uncertainty_score":0.02565497,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3098474649","doi":"10.2196/23811","title":"Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review","year":2020,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":158,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institutes of Health","keywords":"Systematic review; CINAHL; MEDLINE; Pandemic; Coronavirus disease 2019 (COVID-19); Epidemiology; Data science; Grey literature; Medicine; Computer science; Disease; Political science; Pathology; Infectious disease (medical specialty)","authors":[{"name":"Hafsa Bareen Syeda","is_ca":false},{"name":"Mahanazuddin Syed","is_ca":false},{"name":"Kevin W. Sexton","is_ca":false},{"name":"Shorabuddin Syed","is_ca":false},{"name":"Salma Begum","is_ca":false},{"name":"Farhanuddin Syed","is_ca":false},{"name":"Fred Prior","is_ca":false},{"name":"Feliciano Yu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04515323600969522,"gpt":0.4093970256434307,"spread":0.3642437896337354,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01321262,0.001440498,0.008083309,0.01108128,0.0006089009,0.002919519,0.002302284,0.002375623,0.004747169],"category_scores_gemma":[0.07196035,0.0007776353,0.008800745,0.009177718,0.000945288,0.003068452,0.00134975,0.001574177,0.0003622347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003392328,"about_ca_system_score_gemma":0.01266219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00550573,"about_ca_topic_score_gemma":0.01299477,"domain_scores_codex":[0.9904631,0.003903867,0.003285286,0.0006033939,0.00149541,0.0002490893],"domain_scores_gemma":[0.9317564,0.05717894,0.006807747,0.0006097245,0.003318381,0.0003288724],"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":[0.0002114814,0.00002423034,0.001076657,0.9343916,0.01314668,0.00006837431,0.0001144118,0.0002251975,0.00005995821,0.0002906913,0.001425613,0.04896509],"study_design_scores_gemma":[0.0001971196,0.0001799489,0.002609662,0.9224241,0.06048774,0.0002004645,0.0001835442,0.0002673435,0.0001095682,0.0005550837,0.01275047,0.00003495508],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0004791382,0.998428,0.0001769924,0.000341042,0.0000859042,0.0001649283,0.0001659178,0.000006065987,0.0001520286],"genre_scores_gemma":[0.01163105,0.986195,0.0008052757,0.0005754964,0.0001368999,0.0004256948,0.0001603648,0.000005004647,0.00006525166],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01321262,"threshold_uncertainty_score":0.06987584,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2927642672","doi":"10.2196/13064","title":"Clinical Requirements of Future Patient Monitoring in the Intensive Care Unit: Qualitative Study","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":146,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft","keywords":"Usability; Patient safety; Medicine; Medical emergency; Interoperability; Situation awareness; Workload; Intensive care unit; Guideline; Intensive care; Remote patient monitoring; Health care; Nursing; Intensive care medicine; Computer science","authors":[{"name":"Akira-Sebastian Poncette","is_ca":false},{"name":"Claudia Spies","is_ca":false},{"name":"Lina Mosch","is_ca":false},{"name":"Monique Schieler","is_ca":false},{"name":"Steffen Weber‐Carstens","is_ca":false},{"name":"Henning Krampe","is_ca":false},{"name":"Felix Balzer","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1195997942315243,"gpt":0.4955384760203954,"spread":0.3759386817888711,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02549968,0.0005513689,0.00083406,0.001063631,0.005459259,0.00331303,0.001723439,0.001729163,0.001897438],"category_scores_gemma":[0.03833232,0.0006577809,0.0004644629,0.001001167,0.006849121,0.003563288,0.003601837,0.002554258,0.0002321895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005260518,"about_ca_system_score_gemma":0.004921719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004002076,"about_ca_topic_score_gemma":0.004817773,"domain_scores_codex":[0.9785174,0.01717107,0.0006986984,0.0007349069,0.001317313,0.001560622],"domain_scores_gemma":[0.9428373,0.04647524,0.003247158,0.0007366822,0.003735665,0.00296795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003526558,0.00004718934,0.003322106,0.0002045619,0.000004696591,0.0003730741,0.9915373,0.00004044707,0.0009169882,0.0004614485,0.0002473349,0.002809583],"study_design_scores_gemma":[0.000004391641,0.00007424186,0.00179546,0.0001657648,0.000003226098,0.0001585852,0.9947308,0.0001148145,0.0003564464,0.0001448867,0.002437259,0.00001406103],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925141,0.0003753795,0.003210987,0.001678669,0.00004080306,0.0002410444,0.0001090296,0.00001604602,0.001813986],"genre_scores_gemma":[0.9966445,0.000432362,0.00130194,0.0005676131,0.00001280723,0.0002929147,0.00004224039,0.00001541597,0.0006901898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02549968,"threshold_uncertainty_score":0.1348568,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2044738929","doi":"10.2196/medinform.3525","title":"Dynamic Consent: A Possible Solution to Improve Patient Confidence and Trust in How Electronic Patient Records Are Used in Medical Research","year":2015,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":145,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Economic and Social Research Council; Medical Research Council; Versus Arthritis; Wellcome Trust","keywords":"Internet privacy; Leverage (statistics); Health care; Public trust; Medical record; Informed consent; Public health; Data collection; Medicine; Business; Medical emergency; Public relations; Computer science; Nursing; Alternative medicine; Political science","authors":[{"name":"Hawys Williams","is_ca":false},{"name":"Karen Spencer","is_ca":false},{"name":"Caroline Sanders","is_ca":false},{"name":"David J. Lund","is_ca":false},{"name":"Edgar A. Whitley","is_ca":false},{"name":"Jane Kaye","is_ca":false},{"name":"William G Dixon","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.222121077537781,"gpt":0.5227497604227325,"spread":0.3006286828849515,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1685936,0.0009274897,0.0009429142,0.001972809,0.00575251,0.01539741,0.004543747,0.0136847,0.009254703],"category_scores_gemma":[0.3098186,0.001455481,0.001833208,0.001833379,0.02590533,0.03622664,0.02212744,0.01280551,0.0030971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0062569,"about_ca_system_score_gemma":0.01931731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001609889,"about_ca_topic_score_gemma":0.001671778,"domain_scores_codex":[0.7691838,0.1957979,0.00841553,0.008778383,0.01259985,0.005224524],"domain_scores_gemma":[0.6467992,0.2261749,0.02738483,0.06835901,0.01843375,0.01284835],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003445966,0.0002651548,0.005196254,0.0004514821,0.00009317312,0.0005384361,0.02299762,0.001796877,0.00136771,0.8104972,0.01688485,0.1395667],"study_design_scores_gemma":[0.0002716723,0.0003080106,0.001006724,0.0007954119,0.00005925095,0.0006835059,0.003743473,0.003888901,0.001250724,0.8976634,0.09017228,0.000156691],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.03003936,0.002093301,0.4114947,0.463661,0.001367178,0.001628384,0.0002137249,0.0007931357,0.08870924],"genre_scores_gemma":[0.7841594,0.001582874,0.1500932,0.04789649,0.001341617,0.002378057,0.0002066588,0.0002372411,0.01210442],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8314064,"threshold_uncertainty_score":0.8916191,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3045837041","doi":"10.2196/21798","title":"AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":143,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Medical Research Council; Medical Research Council","keywords":"Health records; Clinical decision support system; Medical record; Electronic health record; Computer science; Risk stratification; Electronic medical record; Machine learning; Health care; Generator (circuit theory); Artificial intelligence; Data mining; Medicine; Medical emergency; Decision support system; Internal medicine","authors":[{"name":"Feng Xie","is_ca":false},{"name":"Bibhas Chakraborty","is_ca":false},{"name":"Marcus Eng Hock Ong","is_ca":false},{"name":"Benjamin A. Goldstein","is_ca":false},{"name":"Nan Liu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1200558269226375,"gpt":0.4225546257764097,"spread":0.3024987988537723,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005436106,0.00102728,0.0007469135,0.002633536,0.000257591,0.001086777,0.001337389,0.0005991717,0.003898959],"category_scores_gemma":[0.01862403,0.0004275036,0.0008371266,0.001475356,0.0003598378,0.001144693,0.001476225,0.0009685212,0.001415698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005524631,"about_ca_system_score_gemma":0.001363607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002105215,"about_ca_topic_score_gemma":0.002570317,"domain_scores_codex":[0.9980736,0.0007583943,0.0002150666,0.0003879144,0.0004990193,0.00006594144],"domain_scores_gemma":[0.992361,0.004648344,0.0006472496,0.0007848491,0.001320762,0.0002377648],"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":[0.000951884,0.0004338917,0.06000945,0.0003451117,0.0003587318,0.0004650375,0.0002096407,0.083432,0.004769949,0.004319014,0.03992948,0.8047759],"study_design_scores_gemma":[0.0001985451,0.0003706404,0.01342244,0.00006151919,0.00006696799,0.0004177576,0.00004195732,0.9628322,0.00796868,0.007007692,0.007533972,0.00007756933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07693839,0.000280443,0.8665345,0.0007082795,0.0001415639,0.000866073,0.006687415,0.04632247,0.001520844],"genre_scores_gemma":[0.352432,0.0002369649,0.6309018,0.0003065036,0.0001492828,0.00102723,0.01237893,0.0009655033,0.001601788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005436106,"threshold_uncertainty_score":0.02874923,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2996889063","doi":"10.2196/16492","title":"Analyzing Medical Research Results Based on Synthetic Data and Their Relation to Real Data Results: Systematic Comparison From Five Observational Studies","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Synthetic data; Computer science; Data mining; Observational study; Process (computing); Relation (database); Statistics; Artificial intelligence; Mathematics","authors":[{"name":"Anat Reiner‐Benaim","is_ca":false},{"name":"Ronit Almog","is_ca":false},{"name":"Yuri Gorelik","is_ca":false},{"name":"Irit Hochberg","is_ca":false},{"name":"Laila Nassar","is_ca":false},{"name":"Tanya Mashiach","is_ca":false},{"name":"Mogher Khamaisi","is_ca":false},{"name":"Yael Lurie","is_ca":false},{"name":"Zaher S. Azzam","is_ca":false},{"name":"Johad Khoury","is_ca":false},{"name":"Daniel Kurnik","is_ca":false},{"name":"Rafael Beyar","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2485650051275908,"gpt":0.4320103665125674,"spread":0.1834453613849766,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1946042,0.00120945,0.002498468,0.01491953,0.0009378605,0.00258898,0.001985704,0.001377694,0.001169016],"category_scores_gemma":[0.4085211,0.0008841678,0.008847702,0.009075899,0.003161896,0.002085742,0.003351092,0.001025491,0.0001385527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002146677,"about_ca_system_score_gemma":0.003607531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001165646,"about_ca_topic_score_gemma":0.001680866,"domain_scores_codex":[0.730803,0.1644115,0.06753977,0.01581201,0.02039211,0.00104167],"domain_scores_gemma":[0.263764,0.609698,0.07088684,0.03441085,0.02048339,0.0007569729],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003371543,0.0003902441,0.8365974,0.03173845,0.06125751,0.0009306737,0.008782197,0.004777679,0.001403637,0.001751325,0.0007556195,0.04824371],"study_design_scores_gemma":[0.001828363,0.0105637,0.7241592,0.03887127,0.1350406,0.003908102,0.0222334,0.02806573,0.01043928,0.006680596,0.01766874,0.0005410205],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8725626,0.0474973,0.06877968,0.0003263021,0.0002385577,0.005722303,0.00378635,0.0001277251,0.0009592035],"genre_scores_gemma":[0.971609,0.002838451,0.02034033,0.0001521134,0.00004247771,0.003273816,0.001660528,0.00003874728,0.00004457781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8053958,"threshold_uncertainty_score":0.9931964,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2947577769","doi":"10.2196/13802","title":"Assessing the Availability of Data on Social and Behavioral Determinants in Structured and Unstructured Electronic Health Records: A Retrospective Analysis of a Multilevel Health Care System","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","cited_by":135,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Center for Advancing Translational Sciences","keywords":"Unstructured data; Health care; Medicine; Population; Data collection; Social determinants of health; Medical record; Health information technology; Population health; Medical emergency; Computer science; Big data; Public health; Nursing; Data mining; Environmental health","authors":[{"name":"Elham Hatef","is_ca":false},{"name":"Masoud Rouhizadeh","is_ca":false},{"name":"Iddrisu Tia","is_ca":false},{"name":"Elyse C. Lasser","is_ca":false},{"name":"Felicia Hill‐Briggs","is_ca":false},{"name":"Jill A. Marsteller","is_ca":false},{"name":"Hadi Kharrazi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1616464065474127,"gpt":0.5235938640613984,"spread":0.3619474575139857,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009034933,0.0002640543,0.0004080764,0.002879516,0.001078557,0.001347528,0.0007381102,0.0003797978,0.0005640427],"category_scores_gemma":[0.03295768,0.0004710965,0.0009792327,0.003666005,0.0007362272,0.001396101,0.00251914,0.0008939063,0.0001171582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001884595,"about_ca_system_score_gemma":0.002169981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02841434,"about_ca_topic_score_gemma":0.03539514,"domain_scores_codex":[0.9923156,0.00287031,0.001793895,0.000973737,0.001499741,0.0005467024],"domain_scores_gemma":[0.9586894,0.009114768,0.02135762,0.004035284,0.005315507,0.001487366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002284146,0.00001489944,0.998768,0.00001005784,0.00005501599,0.00001787016,0.000300895,0.00003430231,0.0000388689,0.00002053555,0.00007404311,0.0006426612],"study_design_scores_gemma":[0.000004680321,0.0000877924,0.9971674,0.00003061926,0.00005498767,0.00009362767,0.001470761,0.0005738706,0.0001148508,0.00002823343,0.0003634052,0.000009749502],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966171,0.0001758193,0.001009554,0.0001750055,0.000005334758,0.0001036568,0.00163954,0.000009299707,0.0002645396],"genre_scores_gemma":[0.9968136,0.00009394115,0.001370967,0.00008048365,0.00000834799,0.000107406,0.001466067,0.000003868332,0.00005543268],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02841434,"threshold_uncertainty_score":0.05649787,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4223646541","doi":"10.2196/32578","title":"The Use of Artificial Intelligence–Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical Recommendations","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":135,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Chatbot; CINAHL; PsycINFO; MEDLINE; Weight loss; Overweight; Scopus; Artificial intelligence; Personalization; Medicine; World Wide Web; Computer science; Psychology; Obesity; Psychological intervention; Internal medicine; Nursing","authors":[{"name":"Han Shi Jocelyn Chew","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2219394628413439,"gpt":0.4957736810802575,"spread":0.2738342182389136,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03785183,0.002130948,0.005346975,0.01796377,0.001864927,0.00699043,0.003967875,0.005040629,0.005764525],"category_scores_gemma":[0.1212534,0.001520731,0.007614498,0.01233657,0.002596464,0.008339374,0.004025308,0.003548629,0.001007718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006825914,"about_ca_system_score_gemma":0.02573473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01133912,"about_ca_topic_score_gemma":0.0198031,"domain_scores_codex":[0.97644,0.01194648,0.006841632,0.001055973,0.003063233,0.000652598],"domain_scores_gemma":[0.845812,0.1301975,0.01066598,0.001722363,0.01072689,0.0008753622],"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":[0.0001907083,0.00007145363,0.0005612173,0.7002498,0.001368749,0.0001201061,0.00167538,0.0002090065,0.0002041399,0.001934814,0.005449399,0.2879652],"study_design_scores_gemma":[0.00004443058,0.00008085313,0.000704847,0.9579616,0.003537157,0.0001182155,0.0009576367,0.00009704263,0.0001424528,0.0007784477,0.03555274,0.00002456413],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003965863,0.9959014,0.0004507084,0.001749953,0.0003834515,0.0003613693,0.0000618893,0.00001552845,0.0006790821],"genre_scores_gemma":[0.004856918,0.990591,0.002031044,0.0009776389,0.0001479413,0.001124886,0.00006958134,0.000009546203,0.0001913402],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.03785183,"threshold_uncertainty_score":0.200182,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4386710076","doi":"10.2196/48808","title":"ChatGPT-Generated Differential Diagnosis Lists for Complex Case–Derived Clinical Vignettes: Diagnostic Accuracy Evaluation","year":2023,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":135,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Differential diagnosis; Medical diagnosis; Medicine; Radiology; Pathology","authors":[{"name":"Takanobu Hirosawa","is_ca":false},{"name":"Ren Kawamura","is_ca":false},{"name":"Yukinori Harada","is_ca":false},{"name":"Kazuya Mizuta","is_ca":false},{"name":"Kazuki Tokumasu","is_ca":false},{"name":"Yuki Kaji","is_ca":false},{"name":"Tomoharu Suzuki","is_ca":false},{"name":"Taro Shimizu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3661866926666326,"gpt":0.5378131142135208,"spread":0.1716264215468882,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03211575,0.00100449,0.0009886888,0.008359323,0.0006669479,0.001709628,0.00187581,0.001124492,0.00423564],"category_scores_gemma":[0.2288733,0.0005448966,0.001553309,0.002667364,0.0005922162,0.002119879,0.003074906,0.0008355965,0.001764208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001319803,"about_ca_system_score_gemma":0.001682201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001555095,"about_ca_topic_score_gemma":0.002389101,"domain_scores_codex":[0.9779007,0.01407412,0.00304724,0.001720924,0.002903553,0.0003535287],"domain_scores_gemma":[0.6340344,0.3108282,0.01905411,0.01292482,0.02028402,0.002874489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.008167415,0.001431406,0.4738343,0.008788342,0.001273639,0.00280529,0.008086057,0.01337016,0.005511296,0.001606657,0.02458216,0.4505432],"study_design_scores_gemma":[0.002527845,0.00488821,0.3251768,0.004716713,0.003426142,0.0162405,0.008362578,0.5476813,0.03700813,0.007730876,0.0414755,0.0007654444],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8614919,0.003478621,0.1030315,0.001417771,0.0005923267,0.006779505,0.009579294,0.007783015,0.005846113],"genre_scores_gemma":[0.8147016,0.001340043,0.1700369,0.0003997716,0.0002305325,0.003369842,0.008713664,0.0002685546,0.000939045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03211575,"threshold_uncertainty_score":0.1698463,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3104873923","doi":"10.2196/16503","title":"Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis","year":2020,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Preterm Birth and Chorioamnionitis","field":"Medicine","cited_by":134,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan","keywords":"Logistic regression; Medicine; Multivariable calculus; Pregnancy; Population; Receiver operating characteristic; Machine learning; Scopus; MEDLINE; Predictive modelling; Systematic review; Meta-analysis; Artificial intelligence; Computer science; Internal medicine","authors":[{"name":"Herdiantri Sufriyana","is_ca":false},{"name":"Atina Husnayain","is_ca":false},{"name":"Ya-Lin Chen","is_ca":false},{"name":"Chao-Yang Kuo","is_ca":false},{"name":"Onkar Singh","is_ca":false},{"name":"Tso-Yang Yeh","is_ca":false},{"name":"Yu‐Wei Wu","is_ca":false},{"name":"Emily Chia‐Yu Su","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2103419185048788,"gpt":0.4610943651254286,"spread":0.2507524466205497,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","metaepi_broad"],"consensus_categories":[],"category_scores_codex":[0.04926848,0.003576025,0.01906637,0.009973838,0.0006425602,0.004625885,0.002945119,0.002327705,0.003777166],"category_scores_gemma":[0.1364712,0.001464032,0.05669293,0.009915879,0.0008820835,0.003559863,0.001896784,0.002782391,0.0004024866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003579261,"about_ca_system_score_gemma":0.005488234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00674698,"about_ca_topic_score_gemma":0.01075197,"domain_scores_codex":[0.9625712,0.02148842,0.009541153,0.002610561,0.003325132,0.000463528],"domain_scores_gemma":[0.8885784,0.09611274,0.00903713,0.002246879,0.003730852,0.0002940717],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.00133258,0.00002105722,0.003257939,0.4165047,0.5553778,0.00005414125,0.00006938428,0.001280611,0.00007853861,0.0003405277,0.0007251878,0.0209575],"study_design_scores_gemma":[0.000777384,0.0002059957,0.00222037,0.05240584,0.9401143,0.00007144479,0.00003814577,0.000862763,0.000114063,0.0007244088,0.002429265,0.00003607376],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001872498,0.9931718,0.002771473,0.0003075202,0.0002064402,0.0006591137,0.0007109835,0.00005185559,0.0002483948],"genre_scores_gemma":[0.1366757,0.841343,0.01263852,0.001522166,0.0006056767,0.005003175,0.001713479,0.0001098816,0.0003884005],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9809336,"threshold_uncertainty_score":0.2605597,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2211648215","doi":"10.2196/medinform.5215","title":"Outcomes From Health Information Exchange: Systematic Review and Future Research Needs","year":2015,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":134,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"U.S. National Library of Medicine; Agency for Healthcare Research and Quality; U.S. Department of Health and Human Services","keywords":"Health information exchange; Systematic review; Computer science; Data science; MEDLINE; Medicine; Health care; Knowledge management; Health information; Political science","authors":[{"name":"William Hersh","is_ca":false},{"name":"Annette M Totten","is_ca":false},{"name":"Karen Eden","is_ca":false},{"name":"Beth Devine","is_ca":false},{"name":"Paul Gorman","is_ca":false},{"name":"Steven Z. Kassakian","is_ca":false},{"name":"Susan Woods","is_ca":false},{"name":"Monica Daeges","is_ca":false},{"name":"Miranda Pappas","is_ca":false},{"name":"Marian McDonagh","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1509178671729377,"gpt":0.5194668474988611,"spread":0.3685489803259234,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1218338,0.002192541,0.01548506,0.01812768,0.001470188,0.006989517,0.003814889,0.004849751,0.009222458],"category_scores_gemma":[0.3101447,0.001986612,0.01432289,0.02060103,0.00344287,0.01166198,0.004495304,0.00447933,0.0005766609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0137475,"about_ca_system_score_gemma":0.05153451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01279619,"about_ca_topic_score_gemma":0.03189818,"domain_scores_codex":[0.8885325,0.05699219,0.03451231,0.003765138,0.01363724,0.002560499],"domain_scores_gemma":[0.6307325,0.2967691,0.03935135,0.005291877,0.02549303,0.002362176],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001765639,0.00002556358,0.0007093868,0.9489316,0.006481591,0.00006369609,0.000434072,0.0001216113,0.00003364095,0.001074838,0.003397336,0.03855013],"study_design_scores_gemma":[0.0002288681,0.0001167692,0.001603216,0.962125,0.01993542,0.0001134834,0.0006856133,0.0001018903,0.00004911911,0.002181915,0.01282143,0.00003735734],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0008402257,0.9905195,0.0005707527,0.005258878,0.0004402277,0.001079562,0.0007829863,0.00002226184,0.0004855524],"genre_scores_gemma":[0.02274701,0.9593691,0.003622807,0.006359688,0.0005196055,0.006178237,0.0009821659,0.00002466002,0.0001967341],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8781663,"threshold_uncertainty_score":0.6443262,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4393870852","doi":"10.2196/50048","title":"Toward Fairness, Accountability, Transparency, and Ethics in AI for Social Media and Health Care: Scoping Review","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":130,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"York University; University of Waterloo; Saint Mary's University; Lakehead University","funders":"","keywords":"Accountability; Transparency (behavior); Social media; Health care; Public relations; Psychology; Internet privacy; Political science; Sociology; Computer science; World Wide Web; Computer security","authors":[{"name":"Aditya Singhal","is_ca":true},{"name":"Nikita Neveditsin","is_ca":true},{"name":"Hasnaat Tanveer","is_ca":true},{"name":"Vijay Mago","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3493996880040418,"gpt":0.5522561175184422,"spread":0.2028564295144004,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05350492,0.001451381,0.003431063,0.02696673,0.002839215,0.01134855,0.003013538,0.007161396,0.003845427],"category_scores_gemma":[0.2025411,0.001306213,0.003806737,0.02490668,0.008180753,0.01194881,0.005843123,0.005396963,0.0006543729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01046252,"about_ca_system_score_gemma":0.04635409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01137136,"about_ca_topic_score_gemma":0.01808229,"domain_scores_codex":[0.9610128,0.02089406,0.008236606,0.001742143,0.00735188,0.0007624435],"domain_scores_gemma":[0.6672046,0.2963924,0.01343307,0.003649968,0.01825963,0.001060401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0000732214,0.00007620636,0.001228279,0.4324048,0.001189282,0.0002401294,0.00550139,0.001063442,0.0002204297,0.08006807,0.01634093,0.4615939],"study_design_scores_gemma":[0.0000148582,0.00003487937,0.0008216094,0.8667542,0.001060042,0.0002053459,0.002283706,0.0002580021,0.0001456731,0.01742702,0.1109522,0.00004231915],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003759372,0.9892492,0.001374231,0.005866397,0.0004550259,0.0001560305,0.00004586767,0.000008912041,0.002468377],"genre_scores_gemma":[0.009201639,0.9840646,0.003016688,0.002530197,0.0003904043,0.0005070072,0.00006783084,0.00001219385,0.0002094786],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.05350492,"threshold_uncertainty_score":0.2829644,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2592665192","doi":"10.2196/medinform.6730","title":"Patient Similarity in Prediction Models Based on Health Data: A Scoping Review","year":2017,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":130,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Context (archaeology); Computer science; Similarity (geometry); Scopus; Data mining; Field (mathematics); Predictive modelling; Data science; Health care; MEDLINE; Information retrieval; Medicine; Artificial intelligence; Machine learning","authors":[{"name":"Anis Sharafoddini","is_ca":true},{"name":"Joel A. Dubin","is_ca":true},{"name":"Joon Lee","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2501120553890433,"gpt":0.4906599825597756,"spread":0.2405479271707323,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03729006,0.001894245,0.007045378,0.0215144,0.0007710143,0.005399377,0.003415058,0.003465018,0.003075203],"category_scores_gemma":[0.2142085,0.001824015,0.009687553,0.01884861,0.001512475,0.006973863,0.002234047,0.002466341,0.0004295386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004705471,"about_ca_system_score_gemma":0.01383104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007396219,"about_ca_topic_score_gemma":0.007884371,"domain_scores_codex":[0.9713708,0.01388709,0.0083995,0.001895756,0.004114996,0.0003318598],"domain_scores_gemma":[0.7048749,0.2718029,0.01258571,0.002918154,0.007448875,0.000369328],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0002231601,0.00007461266,0.003199082,0.7357981,0.01057792,0.0001959629,0.0005573076,0.002028426,0.00008578623,0.002501015,0.00334254,0.2414161],"study_design_scores_gemma":[0.00008994387,0.0001803563,0.003124784,0.9379633,0.02563534,0.0003820491,0.0005419307,0.001797086,0.0002071796,0.003346832,0.02665995,0.00007137389],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0008046854,0.9957628,0.001604782,0.0006233555,0.0001519126,0.0002821493,0.0003292087,0.00001946507,0.0004217366],"genre_scores_gemma":[0.0170956,0.9779941,0.003132697,0.000425418,0.0001885847,0.0006764118,0.000413405,0.00001194092,0.00006182435],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.96271,"threshold_uncertainty_score":0.1972111,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3082085807","doi":"10.2196/20477","title":"Applying Blockchain Technology to Address the Crisis of Trust During the COVID-19 Pandemic","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":129,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Blockchain; Computer security; Data sharing; General partnership; Pandemic; Internet privacy; Computer science; Corporate governance; Information privacy; Business; Harm; Government (linguistics); Coronavirus disease 2019 (COVID-19); Political science; Medicine; Law","authors":[{"name":"Anjum Khurshid","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02430840816648665,"gpt":0.3036607728144869,"spread":0.2793523646480002,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005823236,0.0002456808,0.0002795362,0.0006771949,0.001914119,0.003298145,0.0009116826,0.001914862,0.003391794],"category_scores_gemma":[0.01366013,0.0001726497,0.0002992743,0.000768733,0.002283243,0.005077062,0.004520593,0.001562994,0.0007028091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002273761,"about_ca_system_score_gemma":0.004780916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002926406,"about_ca_topic_score_gemma":0.003819576,"domain_scores_codex":[0.9966087,0.002108598,0.0001307906,0.0001808327,0.0006154932,0.000355536],"domain_scores_gemma":[0.992882,0.004212501,0.0005662449,0.0007548049,0.0008755782,0.0007087863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000462704,0.0002624565,0.008863637,0.0008116093,0.00007905656,0.002722371,0.008774888,0.04558893,0.008606675,0.4503997,0.03113755,0.4422905],"study_design_scores_gemma":[0.0001907919,0.000602467,0.001948763,0.001180038,0.00009771527,0.001066605,0.006607009,0.1452666,0.01137159,0.5470161,0.284552,0.0001003061],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2357257,0.008426202,0.4932137,0.1109192,0.001757502,0.0008240175,0.0001919326,0.001134524,0.1478073],"genre_scores_gemma":[0.9278593,0.005531254,0.0537072,0.00214138,0.0002402695,0.0001923629,0.000111774,0.00005339916,0.010163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005823236,"threshold_uncertainty_score":0.03079659,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3033328382","doi":"10.2196/19515","title":"Using Information Technology to Manage the COVID-19 Pandemic: Development of a Technical Framework Based on Practical Experience in China","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"COVID-19 Digital Contact Tracing","field":"Computer Science","cited_by":128,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Health informatics; Informatics; Information technology; Telemedicine; Public health informatics; Computer science; Big data; Cloud computing; The Internet; Health care; Data science; Knowledge management; Medicine; HRHIS; Public health; Health policy; World Wide Web; Engineering; Data mining; Political science; Nursing","authors":[{"name":"Qing Ye","is_ca":false},{"name":"Jin Zhou","is_ca":false},{"name":"Hong Wu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08854208323539685,"gpt":0.4071249031781292,"spread":0.3185828199427324,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01826585,0.0008255843,0.000316136,0.003227165,0.003102251,0.005272206,0.002120047,0.002087721,0.00118808],"category_scores_gemma":[0.006722954,0.0003761481,0.0006764753,0.002682762,0.004214684,0.005911517,0.004199942,0.001587979,0.000274155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01012048,"about_ca_system_score_gemma":0.02361808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03249972,"about_ca_topic_score_gemma":0.02074718,"domain_scores_codex":[0.991655,0.004714895,0.0006951669,0.0005163283,0.001506076,0.0009125595],"domain_scores_gemma":[0.9959058,0.001669021,0.0003290124,0.0004632305,0.001067921,0.0005650689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001024089,0.0009949565,0.06153439,0.002468591,0.0001078214,0.005011386,0.09012841,0.02070941,0.01196357,0.3284785,0.01956166,0.4589388],"study_design_scores_gemma":[0.0001327925,0.001816166,0.0541515,0.006827849,0.0002915938,0.002602255,0.1353947,0.09676425,0.01967379,0.09534346,0.5865332,0.0004684299],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4498923,0.01339385,0.2994329,0.07298191,0.0007065405,0.004881168,0.0003751092,0.0009860288,0.1573501],"genre_scores_gemma":[0.7762696,0.009294912,0.1990151,0.002857041,0.0001705818,0.001070595,0.0004262102,0.00006318361,0.01083275],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03249972,"threshold_uncertainty_score":0.09660023,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4205865577","doi":"10.2196/32875","title":"Operationalizing and Implementing Pretrained, Large Artificial Intelligence Linguistic Models in the US Health Care System: Outlook of Generative Pretrained Transformer 3 (GPT-3) as a Service Model","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":126,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Patient-Centered Outcomes Research Institute","keywords":"Operationalization; Computer science; Software portability; Generative grammar; Health Insurance Portability and Accountability Act; Health care; Transformer; Benchmarking; Artificial intelligence; Knowledge management; Computer security; Engineering; Business","authors":[{"name":"Emre Sezgın","is_ca":false},{"name":"Joseph Sirrianni","is_ca":false},{"name":"Simon Lin Linwood","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.108211482481031,"gpt":0.4219746385367421,"spread":0.3137631560557111,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005615202,0.0007174101,0.000428058,0.0007790023,0.0006602224,0.002550342,0.002198448,0.001018892,0.003841392],"category_scores_gemma":[0.02195572,0.0005540285,0.001111075,0.0009191305,0.001461145,0.003980298,0.002855371,0.002241409,0.0008578451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003692036,"about_ca_system_score_gemma":0.005119499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05046614,"about_ca_topic_score_gemma":0.04871305,"domain_scores_codex":[0.9979156,0.001141638,0.0001455242,0.0002744659,0.0003853312,0.0001374958],"domain_scores_gemma":[0.9913083,0.005691437,0.0003330492,0.001027817,0.001383216,0.0002562499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004475033,0.0004316367,0.01434562,0.0002652484,0.0001405502,0.0003305833,0.002070931,0.7668052,0.003653211,0.07231926,0.00712979,0.1320605],"study_design_scores_gemma":[0.00002032887,0.00004652325,0.0003238721,0.00001777665,0.00001692929,0.00002686109,0.0001270408,0.9832317,0.001030515,0.01361979,0.001523585,0.0000151001],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2301481,0.0002632319,0.7387535,0.004760684,0.0001231432,0.0007060803,0.00133426,0.01019961,0.01371136],"genre_scores_gemma":[0.6454634,0.000181091,0.3494413,0.000542325,0.00002750249,0.0003717545,0.00161844,0.000470488,0.001883695],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05046614,"threshold_uncertainty_score":0.1003448,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2612815242","doi":"10.2196/medinform.7271","title":"Virtual Reality as an Adjunct Home Therapy in Chronic Pain Management: An Exploratory Study","year":2017,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Pediatric Pain Management Techniques","field":"Medicine","cited_by":124,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Physical therapy; Adjunct; Chronic pain; Brief Pain Inventory; Virtual reality; Adverse effect; Intervention (counseling); Rating scale; Psychology; Internal medicine; Psychiatry","authors":[{"name":"Bernie Garrett","is_ca":true},{"name":"Tarnia Taverner","is_ca":true},{"name":"Paul McDade","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03863839473126612,"gpt":0.3597144760575411,"spread":0.321076081326275,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001469532,0.0004242637,0.0004062128,0.0007456907,0.0008722628,0.0005881002,0.0004640106,0.0007157025,0.001600442],"category_scores_gemma":[0.002392572,0.0002134917,0.0004368367,0.0004545882,0.0006662853,0.0004106441,0.0008813239,0.0005594269,0.0002439991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000388325,"about_ca_system_score_gemma":0.0005956165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005428383,"about_ca_topic_score_gemma":0.001051643,"domain_scores_codex":[0.9990428,0.0006181185,0.00004764218,0.00004856799,0.0001129834,0.0001297831],"domain_scores_gemma":[0.9990457,0.0005875901,0.0001139828,0.00005478214,0.00006941403,0.0001285327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005767983,0.09214409,0.2841352,0.003482869,0.0002529498,0.06430353,0.2396973,0.001815805,0.0300288,0.001955877,0.001543766,0.2748719],"study_design_scores_gemma":[0.002391679,0.2260911,0.3969218,0.001227719,0.0004843948,0.08729024,0.2475452,0.003685083,0.01086779,0.0008524905,0.02239664,0.0002458392],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987717,0.0002063643,0.0001686423,0.00002135042,0.000003133764,0.0002186074,0.00001206452,0.000001813382,0.0005962865],"genre_scores_gemma":[0.9971122,0.0007773344,0.001090829,0.00006027205,0.00001778459,0.0003615725,0.00002616867,0.000002081012,0.000551819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001600442,"threshold_uncertainty_score":0.00777173,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400270729","doi":"10.2196/54345","title":"Reference Hallucination Score for Medical Artificial Intelligence Chatbots: Development and Usability Study","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":122,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Preprint; Natural language processing; Computer science; Artificial intelligence; Psychology; World Wide Web","authors":[{"name":"Fadi Aljamaan","is_ca":false},{"name":"Mohamad‐Hani Temsah","is_ca":false},{"name":"Ibraheem Altamimi","is_ca":false},{"name":"Ayman Al‐Eyadhy","is_ca":false},{"name":"Amr Jamal","is_ca":false},{"name":"Khalid Alhasan","is_ca":false},{"name":"Tamer A. Mesallam","is_ca":false},{"name":"Mohamed Farahat","is_ca":false},{"name":"Khalid H. Malki","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2320138201162752,"gpt":0.4742133288584646,"spread":0.2421995087421894,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02017564,0.0007976218,0.001380628,0.003893672,0.0005761299,0.001475772,0.0007763566,0.0007372507,0.001427689],"category_scores_gemma":[0.05820855,0.0004005887,0.001846833,0.001396376,0.0008301558,0.001642937,0.002094376,0.0007502132,0.0003732869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001046861,"about_ca_system_score_gemma":0.0007850287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001085591,"about_ca_topic_score_gemma":0.001741023,"domain_scores_codex":[0.9861464,0.00721212,0.002206359,0.0009930948,0.002995448,0.0004466016],"domain_scores_gemma":[0.935267,0.04452989,0.003761663,0.002278612,0.01204841,0.002114418],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006014195,0.007148585,0.5135186,0.005660446,0.001091102,0.001209699,0.04797526,0.002750368,0.02115895,0.0008399196,0.00556716,0.3870658],"study_design_scores_gemma":[0.0009925142,0.03059556,0.8960693,0.0008399382,0.001180673,0.002276234,0.01859554,0.024355,0.01381306,0.0008668157,0.009987402,0.0004280222],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933251,0.0002232746,0.003515361,0.00007741579,0.00003220016,0.001176377,0.0002247575,0.0002267477,0.00119878],"genre_scores_gemma":[0.9825459,0.0002025548,0.01395602,0.00005765334,0.00002996818,0.001748103,0.0005722591,0.00006140018,0.0008262671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9798244,"threshold_uncertainty_score":0.1067002,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4290775674","doi":"10.2196/36199","title":"Application of Artificial Intelligence in Shared Decision Making: Scoping Review","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Patient-Provider Communication in Healthcare","field":"Health Professions","cited_by":120,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false},"ca_institutions":"Université Laval; McGill University Health Centre; Jewish General Hospital; Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"McGill University","keywords":"Systematic review; Grey literature; Psychological intervention; MEDLINE; Health care; Guideline; Inclusion (mineral); Identification (biology); Computer science; Medicine; Artificial intelligence; Data science; Psychology; Nursing","authors":[{"name":"Samira Abbasgholizadeh Rahimi","is_ca":true},{"name":"Michelle Cwintal","is_ca":true},{"name":"Yuhui Huang","is_ca":true},{"name":"Pooria Ghadiri","is_ca":true},{"name":"Roland Grad","is_ca":true},{"name":"Dan Poenaru","is_ca":true},{"name":"Geneviève Gore","is_ca":true},{"name":"Hervé Tchala Vignon Zomahoun","is_ca":true},{"name":"France Légaré","is_ca":true},{"name":"Pierre Pluye","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2432077694218231,"gpt":0.5194289814827286,"spread":0.2762212120609056,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1280544,0.003053423,0.01108952,0.04952591,0.003659338,0.01118562,0.005610404,0.007664297,0.007853311],"category_scores_gemma":[0.3679275,0.002981397,0.01052383,0.04927248,0.004206816,0.01142796,0.007508562,0.004295269,0.001314377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01326872,"about_ca_system_score_gemma":0.0658235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007745562,"about_ca_topic_score_gemma":0.01420164,"domain_scores_codex":[0.8531825,0.06924266,0.05581405,0.00405953,0.01615494,0.001546278],"domain_scores_gemma":[0.6114875,0.3095284,0.03439548,0.009012093,0.03417605,0.001400543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001115916,0.00003664702,0.0005449855,0.8888254,0.002257817,0.0001782136,0.00153083,0.000346169,0.0001461864,0.003098249,0.005358419,0.0975655],"study_design_scores_gemma":[0.0000335709,0.00003227155,0.000295102,0.9794912,0.002721888,0.00008914847,0.0003966201,0.0001292479,0.00008623846,0.001099315,0.01560522,0.00002002605],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001017254,0.9778105,0.004673034,0.003195985,0.0009861079,0.008851916,0.0008520061,0.00005443154,0.002558845],"genre_scores_gemma":[0.01215798,0.9543108,0.01155979,0.001443487,0.0004265852,0.01886375,0.0008614245,0.00003687014,0.0003392397],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.1280544,"threshold_uncertainty_score":0.6772243,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2757433884","doi":"10.2196/medinform.7958","title":"Impact of Electronic Health Records on Long-Term Care Facilities: Systematic Review","year":2017,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Nursing Diagnosis and Documentation","field":"Nursing","cited_by":120,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Long-term care; Business; Health care; Health records; Incentive; Health information technology; Incentive program; Electronic health record; Medicine; Medical emergency; Population; Environmental health; Nursing; Economic growth","authors":[{"name":"Clemens Scott Kruse","is_ca":false},{"name":"Michael Mileski","is_ca":false},{"name":"Alekhya Ganta Vijaykumar","is_ca":false},{"name":"Sneha Vishnampet Viswanathan","is_ca":false},{"name":"Ujwala Suskandla","is_ca":false},{"name":"Yazhini Chidambaram","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0198344199812552,"gpt":0.3984922646788377,"spread":0.3786578446975825,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01959872,0.001463133,0.008459578,0.01298525,0.0009697935,0.003418843,0.002308874,0.002015198,0.004368264],"category_scores_gemma":[0.09783991,0.001230464,0.007859075,0.01823314,0.001303461,0.003626059,0.002380064,0.001600621,0.0003169423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007344575,"about_ca_system_score_gemma":0.02275229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01392142,"about_ca_topic_score_gemma":0.03589428,"domain_scores_codex":[0.9737125,0.009574537,0.009446791,0.001520504,0.00515425,0.0005914507],"domain_scores_gemma":[0.8779753,0.08722889,0.02272221,0.001471178,0.009747361,0.0008550794],"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":[0.000140542,0.00001570209,0.001048683,0.9722898,0.00654072,0.00006996804,0.0002131018,0.00006519058,0.00005200778,0.0001224275,0.0006890737,0.01875291],"study_design_scores_gemma":[0.0001731195,0.0001712915,0.003807922,0.9488658,0.03967746,0.0001922878,0.0003414796,0.00005869052,0.0001124468,0.0001267182,0.006447633,0.00002499598],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002383619,0.9950808,0.0002067997,0.0003498487,0.0001403918,0.0007132036,0.0006561926,0.00001267767,0.0004564611],"genre_scores_gemma":[0.03544131,0.9604346,0.001088743,0.0007688456,0.00009794016,0.001584989,0.0004231331,0.000008868536,0.000151451],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01959872,"threshold_uncertainty_score":0.1036492,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3014693099","doi":"10.2196/15653","title":"Appropriateness of Overridden Alerts in Computerized Physician Order Entry: Systematic Review","year":2020,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":119,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Ministry of Education, India","keywords":"Computerized physician order entry; Computer science; Order entry; Order (exchange); Data science; Medicine; Medical emergency; Data mining; Health care; Business","authors":[{"name":"Tahmina Nasrin Poly","is_ca":false},{"name":"Md. Mohaimenul Islam","is_ca":false},{"name":"Hsuan‐Chia Yang","is_ca":false},{"name":"Yu‐Chuan Li","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0670936181451619,"gpt":0.4609841237750105,"spread":0.3938905056298486,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03339973,0.001818454,0.009866817,0.01500479,0.0007815832,0.003308981,0.002548102,0.002419144,0.00233167],"category_scores_gemma":[0.198942,0.001789065,0.009935068,0.01628194,0.001750654,0.004328587,0.002207895,0.00157654,0.0002245803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006784133,"about_ca_system_score_gemma":0.01782722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008423816,"about_ca_topic_score_gemma":0.02291299,"domain_scores_codex":[0.934094,0.02399929,0.02731831,0.002959534,0.01094659,0.0006822499],"domain_scores_gemma":[0.743575,0.1917581,0.04626833,0.00264667,0.01501372,0.0007381507],"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":[0.0002316833,0.00001608969,0.001987257,0.9611984,0.009847641,0.00009807882,0.0003413737,0.000122418,0.00008387238,0.00009830263,0.0007760897,0.02519896],"study_design_scores_gemma":[0.0002827703,0.0002092415,0.005818557,0.9182169,0.06777345,0.0004120955,0.0003844023,0.0001681965,0.0002960802,0.0001967814,0.006184079,0.00005747818],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003830024,0.9931335,0.0004237528,0.0003753623,0.0001933045,0.001055727,0.0005686501,0.00002288587,0.0003968243],"genre_scores_gemma":[0.07917376,0.9133274,0.003310451,0.001041118,0.0002387511,0.002221244,0.0005164784,0.00002051969,0.0001503294],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.03339973,"threshold_uncertainty_score":0.1766368,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2104793920","doi":"10.2196/medinform.3503","title":"Adoption of Clinical Decision Support in Multimorbidity: A Systematic Review","year":2015,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":118,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Patient Safety Translational Research Centre; Medical Research Council; National Institute for Health and Care Research","keywords":"Decision support system; Clinical decision support system; Systematic review; Computer science; Multimorbidity; MEDLINE; Medicine; Intensive care medicine; Data mining; Chronic disease","authors":[{"name":"Paolo Fraccaro","is_ca":false},{"name":"Mercedes Arguello Castelerio","is_ca":false},{"name":"John Ainsworth","is_ca":false},{"name":"Iain Buchan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1907562015689149,"gpt":0.5116538272873198,"spread":0.3208976257184049,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01617001,0.001353357,0.006533682,0.01095714,0.0009212571,0.003445434,0.001966594,0.0019698,0.003152921],"category_scores_gemma":[0.08518209,0.001191604,0.00705016,0.01404936,0.001231974,0.003301838,0.002115384,0.001452144,0.0002347971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005705637,"about_ca_system_score_gemma":0.01743497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007824121,"about_ca_topic_score_gemma":0.01993576,"domain_scores_codex":[0.977289,0.009877556,0.007489504,0.001346916,0.003567385,0.0004296158],"domain_scores_gemma":[0.8867879,0.09194123,0.01406594,0.001085367,0.005354427,0.0007652374],"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":[0.0001704033,0.00002422796,0.001197708,0.9449093,0.005255255,0.00007784731,0.0003903903,0.00008331615,0.0000700842,0.0001693057,0.0007473488,0.04690474],"study_design_scores_gemma":[0.0001710749,0.0001842469,0.003702288,0.96123,0.023932,0.0002874616,0.0005097454,0.0001018989,0.0001359377,0.0001846683,0.009527286,0.00003345585],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001868915,0.996637,0.0001863027,0.0002575617,0.00007768999,0.0004514346,0.0002155186,0.000008228074,0.0002972713],"genre_scores_gemma":[0.02784036,0.9689366,0.001494261,0.0004738764,0.0000704734,0.000917085,0.0001922338,0.000005588082,0.00006951659],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01617001,"threshold_uncertainty_score":0.08551621,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2802355308","doi":"10.2196/medinform.8403","title":"Implementing an Open Source Electronic Health Record System in Kenyan Health Care Facilities: Case Study","year":2018,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"ICT in Developing Communities","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Medical Research Council; National Institute for Health and Care Research; National Institute of General Medical Sciences; Wellcome Trust","keywords":"Electronic health record; Open source; Health care; Health records; Medical emergency; Medicine; Patient portal; Computer science; Software; Operating system","authors":[{"name":"Naomi Muinga","is_ca":false},{"name":"Steve Magare","is_ca":false},{"name":"Jonathan Monda","is_ca":false},{"name":"Onesmus Kamau","is_ca":false},{"name":"Stuart Houston","is_ca":false},{"name":"Hamish Fraser","is_ca":false},{"name":"John Powell","is_ca":false},{"name":"Mike English","is_ca":false},{"name":"Chris Paton","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04300816973493849,"gpt":0.3742949347614814,"spread":0.3312867650265429,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006080838,0.000649255,0.0004595735,0.001712101,0.01342266,0.002947042,0.002724376,0.004531515,0.002557412],"category_scores_gemma":[0.01269954,0.000841499,0.0005369326,0.002230253,0.003551971,0.002779595,0.003698884,0.002599401,0.0003097343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006315977,"about_ca_system_score_gemma":0.007154394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02166696,"about_ca_topic_score_gemma":0.06075496,"domain_scores_codex":[0.992334,0.004585622,0.0003561666,0.0003743697,0.0007471084,0.001602667],"domain_scores_gemma":[0.9886902,0.005640837,0.002330381,0.0004424395,0.001071331,0.001824844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002808405,0.00514114,0.09555552,0.002112913,0.00009681367,0.1417391,0.6789727,0.001283963,0.004275864,0.006966913,0.004103538,0.05947063],"study_design_scores_gemma":[0.00007615305,0.002886381,0.05536301,0.001802267,0.00009382451,0.03139335,0.867606,0.001801634,0.003471402,0.0005508675,0.03479516,0.0001598662],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922415,0.0004181388,0.001180601,0.001597695,0.00001956379,0.000417202,0.00005147621,0.00001204967,0.004061845],"genre_scores_gemma":[0.990537,0.001320042,0.004792484,0.0008979423,0.00002751658,0.0002826544,0.00005303064,0.00001476855,0.002074687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02166696,"threshold_uncertainty_score":0.04582584,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}