{"id":"W7133064831","doi":"","title":"Automating Patient Safety Event Report Classification using Natural Language Processing and Machine Learning","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Patient Safety and Medication Errors","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Patient safety; Event (particle physics); Health care; Prioritization; Process (computing); Quality (philosophy); Harm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007010225,0.001773008,0.001194123,0.004341958,0.0007767385,0.002678451,0.003189436,0.001537742,0.001888472],"category_scores_gemma":[0.022545,0.0004818683,0.001368247,0.002537016,0.0007268265,0.003631237,0.001322012,0.002399533,0.002077179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0017892,"about_ca_system_score_gemma":0.003202706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01124124,"about_ca_topic_score_gemma":0.01393458,"domain_scores_codex":[0.9939051,0.002713494,0.0006300637,0.001359219,0.001122449,0.0002695635],"domain_scores_gemma":[0.9589071,0.02995703,0.003665223,0.00283434,0.004180976,0.0004552074],"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":[0.0008131232,0.002624494,0.02587057,0.001423743,0.0002065709,0.0004815191,0.001346147,0.06285307,0.03403092,0.002655673,0.02190091,0.8457932],"study_design_scores_gemma":[0.0000775014,0.0003074371,0.006753037,0.00009681091,0.00006464254,0.0002226393,0.0004677371,0.9576995,0.02115587,0.005414923,0.007675157,0.00006475108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1455011,0.001227736,0.811901,0.002727949,0.0003937228,0.001710015,0.004569038,0.02819023,0.00377911],"genre_scores_gemma":[0.3577586,0.0004729525,0.6273041,0.0007397565,0.000230786,0.0007656433,0.0109373,0.0002922008,0.001498488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01124124,"threshold_uncertainty_score":0.03707403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07039760926693102,"score_gpt":0.4633656556963539,"score_spread":0.3929680464294229,"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."}}