{"id":"W4391550212","doi":"10.2196/54838","title":"Global Trends of Medical Misadventures Using International Classification of Diseases, Tenth Revision Cluster Y62-Y69 Comparing Pre–, Intra–, and Post–COVID-19 Pandemic Phases: Protocol for a Retrospective Analysis Using the TriNetX Platform","year":2024,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministero della Salute","keywords":"Preprint; Pandemic; Coronavirus disease 2019 (COVID-19); Cluster (spacecraft); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Medicine; Virology; Computer science; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03141217,0.001341178,0.001322825,0.003270179,0.001441897,0.001501381,0.001859126,0.00123206,0.01623338],"category_scores_gemma":[0.03574127,0.0008891718,0.002568215,0.003066154,0.001186828,0.001590955,0.002451903,0.002026019,0.003017287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002257092,"about_ca_system_score_gemma":0.009607483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002618789,"about_ca_topic_score_gemma":0.003844233,"domain_scores_codex":[0.9832504,0.00938928,0.003478594,0.001685595,0.001589451,0.0006066006],"domain_scores_gemma":[0.9753246,0.005552013,0.004595995,0.004139673,0.009526111,0.0008616464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.06193129,0.007962623,0.2924045,0.03119783,0.003380727,0.002285738,0.01202035,0.0177464,0.009461974,0.04066803,0.2670861,0.2538543],"study_design_scores_gemma":[0.01501113,0.01874593,0.5363127,0.01591739,0.001427922,0.0009983055,0.007667096,0.01716457,0.01054195,0.01320065,0.3622169,0.0007954097],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.0355441,0.0004919483,0.02849014,0.0006362238,0.000326528,0.8767983,0.0516994,0.000300344,0.005713068],"genre_scores_gemma":[0.02069617,0.0001968363,0.02082599,0.0002299016,0.00003896721,0.9493335,0.00791145,0.00003619961,0.0007310085],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.03141217,"threshold_uncertainty_score":0.1661254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6650188696844989,"score_gpt":0.7051988889831943,"score_spread":0.04018001929869541,"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."}}