{"id":"W4391256425","doi":"10.1016/j.biopsych.2024.01.012","title":"Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample","year":2024,"lang":"en","type":"article","venue":"Biological Psychiatry","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre for Addiction and Mental Health; University of British Columbia; University Health Network; University of Toronto; St. Michael's Hospital","funders":"National Institute of Mental Health; H. Lundbeck A/S; Vancouver Coastal Health Research Institute; University of Toronto; Michael Smith Health Research BC; MagVenture; Fondation Brain Canada; Canadian Institutes of Health Research; Centre for Addiction and Mental Health Foundation; Weston Brain Institute; National Institute on Drug Abuse; BrainsWay; Indivior; Hope for Depression Research Foundation","keywords":"Categorical variable; Overfitting; Parsing; Multivariate statistics; Depression (economics); Sample (material); Major depressive disorder; Computer science; Propensity score matching; Psychology; Artificial intelligence; Natural language processing; Machine learning; Clinical psychology; Statistics; Mathematics","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":[0.009263023,0.0006724041,0.0008928027,0.002131753,0.001152289,0.002410966,0.001954935,0.00150503,0.002786126],"category_scores_gemma":[0.04619977,0.0004580441,0.001683951,0.002327547,0.00122018,0.001745622,0.001648447,0.001364044,0.0003341559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006884873,"about_ca_system_score_gemma":0.001060102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007315084,"about_ca_topic_score_gemma":0.01220001,"domain_scores_codex":[0.9958547,0.002833642,0.0001921457,0.0007012364,0.0001681211,0.0002501029],"domain_scores_gemma":[0.9767248,0.01828652,0.001307657,0.002009656,0.001048098,0.0006231333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001024221,0.0003546967,0.8353729,0.0003114288,0.002412658,0.001058426,0.002116854,0.03282924,0.006891971,0.02789797,0.007531761,0.08219778],"study_design_scores_gemma":[0.0001944785,0.0002546442,0.4520793,0.0001297587,0.0007853544,0.0009452123,0.003431028,0.33708,0.001063043,0.2017027,0.002160246,0.0001741725],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9084764,0.0005096245,0.0865041,0.001307711,0.00003775387,0.00008947858,0.001981621,0.0002196912,0.0008734694],"genre_scores_gemma":[0.9781433,0.00005549859,0.02004107,0.00007826614,0.00002143953,0.00007288935,0.001298897,0.00003912119,0.0002493687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009263023,"threshold_uncertainty_score":0.04898816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09396261310854628,"score_gpt":0.3090577984624828,"score_spread":0.2150951853539365,"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."}}