{"id":"W4388295883","doi":"10.1177/07067437231210787","title":"Prediction of Early Antidepressant Efficacy in Patients with Major Depressive Disorder Based on Multidimensional Features of rs-fMRI and <i>P11</i> Gene DNA Methylation: Prédiction de l’efficacité précoce d’un antidépresseur chez des patients souffrant du trouble dépressif majeur d’après les caractéristiques multidimensionnelles de la méthylation de l’ADN du gène P11 et de la IRMf-rs","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Psychiatry","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Jiangsu Provincial Key Research and Development Program; National Natural Science Foundation of China","keywords":"Major depressive disorder; DNA methylation; Hamd; Functional magnetic resonance imaging; Methylation; Antidepressant; Feature selection; Artificial intelligence; Random forest; Internal medicine; Medicine; Psychology; Biology; Gene; Genetics; Computer science; Neuroscience; Amygdala; Gene expression","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":[0.000465241,0.0003867829,0.0004114812,0.0003733896,0.0001088683,0.0002964533,0.0001720351,0.0002824897,0.0005098908],"category_scores_gemma":[0.001428851,0.0001173653,0.0003601983,0.0002096543,0.0001076876,0.0001702228,0.0001499652,0.0002603224,0.0001344341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001333605,"about_ca_system_score_gemma":0.0001389295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001357216,"about_ca_topic_score_gemma":0.001855784,"domain_scores_codex":[0.9998178,0.0000637484,0.00002260945,0.00005421128,0.00002364795,0.00001799439],"domain_scores_gemma":[0.9995227,0.0002228681,0.0001478399,0.00002669892,0.00004276631,0.00003709699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009235112,0.00009743446,0.9727534,0.00002303829,0.000180739,0.0001002801,0.00005607177,0.001998742,0.004845983,0.00002807745,0.000141878,0.01885083],"study_design_scores_gemma":[0.00004523008,0.0004391367,0.9710335,0.00000875996,0.0001003868,0.0002255628,0.00005901039,0.026921,0.0009427132,0.00008299235,0.0001320176,0.000009750416],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983387,0.0001384806,0.001180412,0.00004535322,0.000004128502,0.00001067115,0.0001468326,0.000008819709,0.0001266065],"genre_scores_gemma":[0.9990624,0.00004840991,0.0006394968,0.000009460106,0.000006064206,0.000009007917,0.0001458534,8.948098e-7,0.00007842776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001357216,"threshold_uncertainty_score":0.0026986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348753445707942,"score_gpt":0.2321668893936938,"score_spread":0.2186793549366144,"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."}}