{"id":"W2947402118","doi":"10.2196/13946","title":"Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach","year":2019,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Mental Health via Writing","field":"Psychology","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Random forest; Naive Bayes classifier; Logistic regression; Posttraumatic stress; Psychological intervention; Support vector machine; Machine learning; Intervention (counseling); Clinical psychology; Artificial intelligence; Psychology; Medicine; Computer science; Psychiatry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001031397,0.0002988875,0.0004519969,0.0001428613,0.0006317894,0.00003534448,0.0002573092,0.0001267486,0.001546382],"category_scores_gemma":[0.00001369057,0.0002929914,0.00009244164,0.0002800837,0.00004738985,0.000144345,0.0001315041,0.0009954551,0.001003183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003942357,"about_ca_system_score_gemma":0.00007099595,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007737579,"about_ca_topic_score_gemma":0.000325151,"domain_scores_codex":[0.9962832,0.000788066,0.000774931,0.0006605556,0.0004381646,0.001055086],"domain_scores_gemma":[0.9984746,0.0001332228,0.0005735466,0.0004283811,0.00001861308,0.0003716295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001535843,0.0008738177,0.9421666,0.001182217,0.00004000551,0.00000246928,0.02194976,0.00004992589,0.00001395503,0.0005250555,0.0003168342,0.03272582],"study_design_scores_gemma":[0.0106662,0.004954849,0.7980232,0.001491217,0.00002935557,0.000301003,0.1125968,0.06099911,0.00003279532,0.00009319039,0.009571475,0.001240771],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9696568,0.001573429,0.00006786754,0.0004416954,0.000777582,0.00289004,0.0001880589,0.0002855101,0.024119],"genre_scores_gemma":[0.9936273,0.00005373258,0.0009371001,0.0007046638,0.0001465428,0.0002653206,0.000434453,0.00008693384,0.003743999],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1441433,"threshold_uncertainty_score":0.9999522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02560265170945674,"score_gpt":0.3685473931343947,"score_spread":0.342944741424938,"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."}}