{"id":"W4400115818","doi":"10.2196/52773","title":"Predicting the Population Risk of Suicide Using Routinely Collected Health Administrative Data in Quebec, Canada: Model-Based Synthetic Estimation Study","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Suicide and Self-Harm Studies","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Public Health Agency of Canada; Institut National de Santé Publique du Québec; Université Laval; Dalhousie University","funders":"","keywords":"Estimation; Logistic regression; Population; Risk assessment; Public health; Environmental health; Predictive modelling; Poison control; Suicide prevention; Population health; Health care; Medicine; Community health; Actuarial science; Computer science; Machine learning; Engineering; Business; Computer security; Nursing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.008557149,0.0009138485,0.0008754035,0.001246809,0.001172554,0.001170261,0.002135858,0.0007274016,0.001184416],"category_scores_gemma":[0.0189751,0.0004991957,0.001176648,0.001962065,0.0007650287,0.0005827997,0.000919128,0.001098531,0.0002025902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01557003,"about_ca_system_score_gemma":0.01286926,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9634946,"about_ca_topic_score_gemma":0.9192425,"domain_scores_codex":[0.9978151,0.001136789,0.0001120261,0.0004523363,0.00023687,0.0002468756],"domain_scores_gemma":[0.9842858,0.008080352,0.001272658,0.001189055,0.004582719,0.0005894903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006054435,0.0004221184,0.808841,0.0001395453,0.0009415825,0.0002533377,0.0004594359,0.1656619,0.0001881357,0.002037769,0.004233324,0.01621643],"study_design_scores_gemma":[0.00009371541,0.0001065735,0.1640877,0.00005001555,0.000183116,0.00005747462,0.0003589404,0.833454,0.0001185856,0.0004980589,0.0009515097,0.00004028836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830542,0.0004698157,0.009320706,0.0005672731,0.00003156114,0.0002135093,0.005346717,0.0001240244,0.0008722364],"genre_scores_gemma":[0.9878692,0.0002033929,0.005767929,0.0001001716,0.00001442244,0.0001197529,0.005408615,0.00001367696,0.000502886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0365054,"threshold_uncertainty_score":0.112969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1104308169262753,"score_gpt":0.3993791116245632,"score_spread":0.2889482946982879,"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."}}