{"id":"W2470809924","doi":"10.2196/mental.5475","title":"Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records","year":2016,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Suicide and Self-Harm Studies","field":"Psychology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Risk assessment; Medical record; Receiver operating characteristic; Poison control; Medicine; Medical history; Suicide prevention; Mental health; Medical emergency; Psychiatry; Computer science; Internal medicine","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":[0.001676397,0.0006121469,0.0004106408,0.002670498,0.0001897968,0.0008121012,0.0004557405,0.0004072344,0.0008612141],"category_scores_gemma":[0.008471702,0.0002291922,0.0007059917,0.001055702,0.0001389091,0.0005677938,0.0005253477,0.0005931547,0.0003162312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004479894,"about_ca_system_score_gemma":0.0006623391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007542782,"about_ca_topic_score_gemma":0.01089889,"domain_scores_codex":[0.9994943,0.0001947535,0.00007649302,0.0001096798,0.00008898879,0.00003574501],"domain_scores_gemma":[0.9956127,0.002415493,0.001082614,0.0002466737,0.000469161,0.0001732794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001372316,0.0001521433,0.9510363,0.00004936377,0.0001590693,0.00006904018,0.00005534282,0.02003954,0.0002437968,0.00006749318,0.000441056,0.02754964],"study_design_scores_gemma":[0.00002890136,0.0003579332,0.601478,0.00009093736,0.0001783303,0.0002664181,0.000205655,0.3953948,0.000876979,0.0006612841,0.00042951,0.0000312382],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796966,0.0003747714,0.01582803,0.000281938,0.00003133448,0.0001086247,0.002471311,0.0002389464,0.0009685121],"genre_scores_gemma":[0.9899622,0.0001657634,0.00785527,0.00002393493,0.00002155894,0.00002981985,0.001784269,0.000004981686,0.0001522878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007542782,"threshold_uncertainty_score":0.01499778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03226530132575119,"score_gpt":0.3627626846538332,"score_spread":0.330497383328082,"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."}}