{"id":"W4293443642","doi":"10.4088/jcp.21m14178","title":"Machine Learning Prediction of Suicide Risk Does Not Identify Patients Without Traditional Risk Factors","year":2022,"lang":"en","type":"article","venue":"The Journal of Clinical Psychiatry","topic":"Suicide and Self-Harm Studies","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Mental Health; Hamilton Health Sciences Foundation","keywords":"Medicine; Medical diagnosis; Emergency department; Suicide prevention; Risk factor; Poison control; Percentile; Injury prevention; Occupational safety and health; Specialty; Medical record; Risk assessment; Suicide attempt; Emergency medicine; Demography; Family medicine; Psychiatry; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003453515,0.0007026524,0.0009559836,0.001047368,0.0003302047,0.001266324,0.0006294711,0.0007231007,0.001608145],"category_scores_gemma":[0.02664215,0.0003219724,0.00101063,0.0007292868,0.0002953911,0.001223589,0.0008096375,0.001419668,0.0006723361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000384935,"about_ca_system_score_gemma":0.00062242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003456638,"about_ca_topic_score_gemma":0.005417123,"domain_scores_codex":[0.9979895,0.0009427005,0.0002287245,0.0003679063,0.0002692757,0.0002018329],"domain_scores_gemma":[0.9859006,0.008732948,0.002364571,0.001377793,0.00096963,0.0006545325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002650682,0.0001194225,0.9813261,0.00003238956,0.000192172,0.0000787319,0.00004589453,0.006056285,0.00009944163,0.0001129222,0.0006601742,0.01101149],"study_design_scores_gemma":[0.0001143508,0.0007031037,0.6125628,0.0001829917,0.0002803352,0.0006923642,0.0003762564,0.3796646,0.000733188,0.003539116,0.00110455,0.00004638648],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909479,0.0005207686,0.005435008,0.0005642738,0.00006582915,0.00003576643,0.0009783139,0.00006788784,0.001384279],"genre_scores_gemma":[0.9976224,0.0001035229,0.001153469,0.00009493956,0.00004238689,0.00001314753,0.0008047963,0.000006229766,0.000159137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003456638,"threshold_uncertainty_score":0.01826411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0855466643633934,"score_gpt":0.3828574304449099,"score_spread":0.2973107660815165,"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."}}