{"id":"W4400899165","doi":"10.2196/58141","title":"Construction of a Multi-Label Classifier for Extracting Multiple Incident Factors From Medication Incident Reports in Residential Care Facilities: Natural Language Processing Approach","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Patient Safety and Medication Errors","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Incident report; Sentence; Classifier (UML); Artificial intelligence; Health care; Computer science; Patient safety; Medicine; Computer security","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":[0.002228719,0.001510882,0.0007994898,0.002718233,0.001040994,0.001185318,0.001674678,0.001824321,0.001374888],"category_scores_gemma":[0.005851266,0.0004014692,0.001392214,0.001186579,0.0005477583,0.001597814,0.001301589,0.002126206,0.001106538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00147709,"about_ca_system_score_gemma":0.002547989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009515895,"about_ca_topic_score_gemma":0.009947355,"domain_scores_codex":[0.9980938,0.0003521612,0.0002369113,0.0006919001,0.0003682693,0.000257061],"domain_scores_gemma":[0.9965656,0.00170314,0.000338202,0.0001930452,0.001071743,0.0001282489],"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":[0.0006853385,0.001004933,0.03241056,0.0005191636,0.0001992725,0.001781668,0.001289692,0.04298482,0.03978652,0.003209548,0.01919038,0.856938],"study_design_scores_gemma":[0.00003069906,0.0001732516,0.005447562,0.00005616689,0.0001016337,0.0002846032,0.0006357061,0.9716884,0.01399676,0.004060927,0.003477933,0.00004620383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1588216,0.0005597088,0.8253782,0.001141528,0.0003087487,0.0008732729,0.002590543,0.007695087,0.002631275],"genre_scores_gemma":[0.499515,0.0002424322,0.4871159,0.0004582198,0.0001610029,0.0009690901,0.008059053,0.0001589958,0.003320368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009515895,"threshold_uncertainty_score":0.01892096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07044419460699387,"score_gpt":0.4199787773598914,"score_spread":0.3495345827528976,"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."}}