{"id":"W3099029676","doi":"10.1088/1741-2552/abc8d6","title":"Predicting PTSD severity using longitudinal magnetoencephalography with a multi-step learning framework","year":2020,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Canadian Armed Forces; St Joseph's Health Care; Mental Health Research Canada; Hospital for Sick Children","funders":"Canadian Institute for Military and Veteran Health Research; Defence Research and Development Canada","keywords":"Magnetoencephalography; Random forest; Feature selection; Support vector machine; Computer science; Artificial intelligence; Feature (linguistics); Machine learning; Regression; Pattern recognition (psychology); Psychology; Statistics; Mathematics; Neuroscience; Electroencephalography","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":[0.001324054,0.0006984124,0.0005569145,0.0005834442,0.0001866832,0.0005221011,0.0007693659,0.0006671908,0.001395466],"category_scores_gemma":[0.002193949,0.0003392096,0.0008418522,0.0003185017,0.0001754462,0.000558245,0.0006651918,0.001078677,0.0003532038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004175597,"about_ca_system_score_gemma":0.0008222218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005709003,"about_ca_topic_score_gemma":0.007082116,"domain_scores_codex":[0.9998011,0.00007879534,0.0000120966,0.0000607982,0.00002201684,0.00002519651],"domain_scores_gemma":[0.999496,0.0002659563,0.00005398564,0.00003576832,0.0001104135,0.00003787877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0005687571,0.0005363773,0.03320201,0.00006467079,0.0003962028,0.0001863669,0.00007135353,0.8188897,0.005028988,0.0007804184,0.001478984,0.1387962],"study_design_scores_gemma":[0.000004081931,0.00006674038,0.001793021,0.000004591404,0.00001109674,0.00001059064,0.000004813683,0.9974123,0.0003659201,0.0002652764,0.00005685468,0.000004808892],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5415947,0.0005256264,0.4540856,0.0007666377,0.00007509801,0.0001166236,0.0007146572,0.001017475,0.001103664],"genre_scores_gemma":[0.9462609,0.0001109369,0.05170704,0.0000805806,0.00003264363,0.0000940571,0.0005044825,0.00002301783,0.001186298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005709003,"threshold_uncertainty_score":0.01135153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0599675416216142,"score_gpt":0.2623168335956991,"score_spread":0.2023492919740849,"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."}}