{"id":"W4402047114","doi":"10.3390/epidemiologia5030039","title":"Optimising Clinical Epidemiology in Disease Outbreaks: Analysis of ISARIC-WHO COVID-19 Case Report Form Utilisation","year":2024,"lang":"en","type":"article","venue":"Epidemiologia","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Kementerian Kesihatan Malaysia; All-India Institute of Medical Sciences; University of Cape Town; Prince Charles Hospital Foundation; Norges Forskningsråd; Medical Research Council; Kementerian Pendidikan Nasional; Conselho Nacional de Desenvolvimento Científico e Tecnológico; JST-Mirai Program; Ministero della Salute; World Health Organization; Wellcome Trust; University College Dublin; Sunnybrook Research Institute; Institut National de la Santé et de la Recherche Médicale; European Federation of Pharmaceutical Industries and Associations; Medical Research Charities Group; National Institute for Health and Care Research; Canadian Institutes of Health Research; Wellcome; European Commission; Instituto de Salud Carlos III; Bill and Melinda Gates Foundation","keywords":"CRFS; Data collection; Outbreak; Medicine; Data quality; Psychological intervention; Demographics; Epidemiology; Consistency (knowledge bases); Quality management; Clinical trial; Conditional random field; Computer science; Statistics; Internal medicine; Demography; Operations management; Pathology; Nursing; Artificial intelligence; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03830276,0.0002787417,0.001635559,0.001085231,0.0001279363,0.0000348334,0.0007660997,0.000367039,0.00006470219],"category_scores_gemma":[0.1541266,0.0002319644,0.0006385697,0.002825349,0.000251346,0.0003402616,0.0004556205,0.0009555807,0.00001525684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003561162,"about_ca_system_score_gemma":0.0006368119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005402931,"about_ca_topic_score_gemma":0.0003767503,"domain_scores_codex":[0.9886063,0.0055686,0.003485058,0.00145088,0.0002247167,0.0006643985],"domain_scores_gemma":[0.9632862,0.03327585,0.0009859982,0.001620913,0.000116907,0.0007141081],"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":[0.00002439489,0.00004014192,0.8617567,0.0001878438,0.0001756409,0.0142822,0.0006152162,0.08479469,4.073934e-7,0.02563783,0.001135919,0.01134901],"study_design_scores_gemma":[0.00009659605,0.00005922737,0.265997,0.00004497469,0.0001296959,0.0008118709,0.00002727892,0.718914,2.379325e-7,0.01252177,0.001236755,0.0001605815],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2125059,0.00202984,0.7574109,0.02604909,0.0009321871,0.0003574796,0.00002643651,0.0004106716,0.0002774164],"genre_scores_gemma":[0.9411593,0.000134133,0.05357189,0.004764352,0.0001377586,0.00003782445,0.00008996316,0.00001470113,0.00009007323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7286534,"threshold_uncertainty_score":0.9902697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2380035949324436,"score_gpt":0.510106447860877,"score_spread":0.2721028529284334,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}