{"id":"W4394879589","doi":"10.2196/52045","title":"Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation","year":2024,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Treatment of Major Depression","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; Wellcome Trust","keywords":"Operationalization; Machine learning; Artificial intelligence; Computer science; Bayesian network; Receiver operating characteristic; Youden's J statistic; Feature selection; Sensitivity (control systems); Probabilistic logic; Algorithm; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01359424,0.00165545,0.001671281,0.003448532,0.0008661938,0.001460921,0.002295512,0.00192078,0.002941646],"category_scores_gemma":[0.047151,0.00115554,0.001246613,0.001574682,0.000698076,0.001728633,0.001904701,0.003045932,0.0009129986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001842622,"about_ca_system_score_gemma":0.003482516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01736415,"about_ca_topic_score_gemma":0.01369584,"domain_scores_codex":[0.9966686,0.002192439,0.0002402359,0.0004039272,0.0003672195,0.0001275006],"domain_scores_gemma":[0.970641,0.02524526,0.001074799,0.0004792026,0.002291762,0.0002680858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005057505,0.0003678488,0.01689561,0.0002791799,0.0002912774,0.0001214623,0.0002098843,0.706667,0.0007408832,0.00575739,0.003205034,0.2649586],"study_design_scores_gemma":[0.00005082471,0.00003930933,0.000710914,0.00006250379,0.00003332744,0.00003230625,0.0000273069,0.9935011,0.0002386562,0.004871213,0.0004226688,0.000009750205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03084041,0.001203158,0.9634375,0.0007566871,0.0000529619,0.0006786283,0.0003580679,0.001382276,0.001290309],"genre_scores_gemma":[0.2785961,0.001024357,0.7164201,0.0003291995,0.00008900146,0.001464313,0.001037434,0.0001158353,0.0009237463],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01736415,"threshold_uncertainty_score":0.07189405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02084724660459081,"score_gpt":0.346900830193852,"score_spread":0.3260535835892612,"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."}}