{"id":"W4309938770","doi":"10.1016/j.jpsychires.2022.11.030","title":"Predictors of illicit substance abuse/dependence during young adulthood: A machine learning approach","year":2022,"lang":"en","type":"article","venue":"Journal of Psychiatric Research","topic":"Suicide and Self-Harm Studies","field":"Psychology","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Joseph’s Healthcare Hamilton; McMaster University","funders":"Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul; Canadian Institutes of Health Research; Russian Academy of Sciences; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Instituto Nacional de Ciência e Tecnologia Translacional em Medicina; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Substance abuse; Psychiatry; Young adult; Substance dependence; Alcohol dependence; Population; Psychopathology; Clinical psychology; Psychology; Alcohol abuse; Poison control; Medicine; Alcohol; Gerontology; Medical emergency; Environmental health","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.00228255,0.0007604611,0.0006951732,0.001639122,0.0006485994,0.001326048,0.000792601,0.0009982084,0.001661884],"category_scores_gemma":[0.005550383,0.0002916724,0.001227954,0.0009003613,0.0003259134,0.0007466047,0.0005248419,0.001718501,0.0004155407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000426775,"about_ca_system_score_gemma":0.0008621414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009718962,"about_ca_topic_score_gemma":0.008144102,"domain_scores_codex":[0.9995673,0.0001988794,0.0000430597,0.00009441229,0.0000339558,0.00006239057],"domain_scores_gemma":[0.9956337,0.003282465,0.0003471624,0.0001946064,0.0002614169,0.0002806652],"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":[0.0003187029,0.0006014016,0.9735588,0.00002374717,0.0002712359,0.0000636025,0.00009100675,0.005679735,0.0002970023,0.0001498273,0.0005150856,0.01842998],"study_design_scores_gemma":[0.00003994475,0.0005389642,0.614191,0.00006569384,0.0004289281,0.0002293089,0.0006432165,0.3814588,0.0005130663,0.001429874,0.0004250801,0.00003623232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930341,0.000494226,0.004563365,0.0004882398,0.00004193177,0.00004321112,0.0008219553,0.00006634364,0.0004465958],"genre_scores_gemma":[0.9963467,0.0001860396,0.002163632,0.00002854232,0.00004648569,0.00002746108,0.0008217022,0.000006000554,0.0003735019],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009718962,"threshold_uncertainty_score":0.01932478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04742378172108902,"score_gpt":0.3517763945012178,"score_spread":0.3043526127801288,"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."}}