{"id":"W4307338507","doi":"10.1001/jamapsychiatry.2022.3391","title":"Being Precise About Precision Mental Health","year":2022,"lang":"en","type":"article","venue":"JAMA Psychiatry","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto; SickKids Foundation; Centre for Addiction and Mental Health","funders":"","keywords":"Precision medicine; Mental health; MEDLINE; Psychology; Medicine; Computer science; Psychiatry; Political science; Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00116366,0.000173416,0.0002107693,0.0001861869,0.001255813,0.000176509,0.001508684,0.00004951112,0.0002520748],"category_scores_gemma":[0.0000416361,0.0001835947,0.0001047981,0.0006146907,0.00001732023,0.0003663259,0.001062315,0.0008724994,0.00006449666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002722795,"about_ca_system_score_gemma":0.0005595633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002506818,"about_ca_topic_score_gemma":0.00003966322,"domain_scores_codex":[0.9970292,0.0005814045,0.000445862,0.0006486669,0.0007936245,0.0005011841],"domain_scores_gemma":[0.9984857,0.00007786552,0.0002449076,0.0009360507,0.00003338507,0.0002220679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009276163,0.0003188177,0.09141865,0.0001545378,0.00002101144,0.000006454213,0.007824345,0.001437826,0.00001260078,0.104428,0.214171,0.580114],"study_design_scores_gemma":[0.002336748,0.001664789,0.1005163,0.0002081865,0.000004263825,0.0002587403,0.0006008598,0.1025666,0.00001010967,0.02860349,0.7625089,0.0007209157],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2062263,0.01098107,0.08117151,0.633449,0.03971525,0.00253225,0.000102192,0.00221781,0.02360464],"genre_scores_gemma":[0.7413505,0.0001148048,0.2270758,0.02797163,0.001022589,0.0002031691,0.00006825375,0.0000720411,0.002121174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6054773,"threshold_uncertainty_score":0.9658822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009364088829162126,"score_gpt":0.2930790266976775,"score_spread":0.2837149378685154,"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."}}