{"id":"W4386968810","doi":"10.1016/j.jagp.2023.09.009","title":"Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies","year":2023,"lang":"en","type":"article","venue":"American Journal of Geriatric Psychiatry","topic":"Treatment of Major Depression","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; McGill University","funders":"","keywords":"Interpretability; Population; Receiver operating characteristic; Major depressive disorder; Depression (economics); Machine learning; Medicine; Artificial intelligence; Clinical Practice; Clinical psychology; Computer science; Physical therapy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03076787,0.0009078472,0.002297675,0.00280448,0.000991623,0.002721645,0.001540817,0.001403058,0.002333677],"category_scores_gemma":[0.05263244,0.0003282079,0.005950875,0.002458848,0.0006876031,0.001540029,0.002185756,0.002183121,0.0002658496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004927198,"about_ca_system_score_gemma":0.001054969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00284932,"about_ca_topic_score_gemma":0.003973325,"domain_scores_codex":[0.981855,0.01122852,0.002653531,0.002246795,0.00138428,0.0006317631],"domain_scores_gemma":[0.9599718,0.03064381,0.003695039,0.002975289,0.002060732,0.0006533642],"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":[0.009352768,0.0005508403,0.8986569,0.0008455897,0.03242063,0.0004052155,0.001007857,0.005072024,0.003897927,0.0007472957,0.002399893,0.04464301],"study_design_scores_gemma":[0.001710326,0.003600847,0.88405,0.0004351421,0.03280289,0.0009750165,0.002789456,0.06153639,0.002154156,0.005200061,0.004605281,0.0001403463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.980368,0.002421518,0.0130244,0.0007043767,0.0001004065,0.0003010745,0.002459769,0.00006695047,0.000553492],"genre_scores_gemma":[0.9935353,0.0001374794,0.003516484,0.0001551187,0.00003255618,0.0001769341,0.002285944,0.00002922233,0.0001310374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03076787,"threshold_uncertainty_score":0.162718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02542171557359928,"score_gpt":0.3559481988610145,"score_spread":0.3305264832874152,"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."}}