{"id":"W4402552866","doi":"10.2196/59782","title":"Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Topic Modeling","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Computer science; Health care; Artificial intelligence; Data 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":[0.01596708,0.0008298617,0.0006752766,0.004190161,0.0004951549,0.00183384,0.001167661,0.001240797,0.002155337],"category_scores_gemma":[0.06513486,0.0001757853,0.001217508,0.004719267,0.000673913,0.003910183,0.001536701,0.001243271,0.0006969549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00230188,"about_ca_system_score_gemma":0.0009847742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00565494,"about_ca_topic_score_gemma":0.004688838,"domain_scores_codex":[0.9910602,0.004611746,0.0008573111,0.001620954,0.001626251,0.0002235263],"domain_scores_gemma":[0.9337635,0.05423393,0.003451849,0.003101157,0.004851222,0.0005983273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001469997,0.000888341,0.4756131,0.002300424,0.001699641,0.0003201835,0.00128075,0.1664258,0.002474888,0.008426621,0.0190102,0.32009],"study_design_scores_gemma":[0.00006403406,0.0008694557,0.1312514,0.000330289,0.0006198544,0.0006982972,0.001208894,0.8352183,0.008416898,0.01126037,0.00994759,0.0001146238],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.768256,0.009696441,0.1846611,0.003098202,0.0002854101,0.0009118841,0.02185639,0.002111348,0.009123256],"genre_scores_gemma":[0.9449219,0.0007416057,0.03914433,0.0002005469,0.00009005626,0.0002330848,0.01387266,0.00007016659,0.0007257244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01596708,"threshold_uncertainty_score":0.08444303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1318051687556064,"score_gpt":0.4519462519423409,"score_spread":0.3201410831867345,"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."}}