{"id":"W2801822511","doi":"10.1097/j.pain.0000000000001264","title":"Multivariate machine learning distinguishes cross-network dynamic functional connectivity patterns in state and trait neuropathic pain","year":2018,"lang":"en","type":"article","venue":"Pain","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Western Hospital; University of Toronto; University Health Network","funders":"","keywords":"Dynamic functional connectivity; Default mode network; Trait; Chronic pain; Functional connectivity; Salience (neuroscience); Resting state fMRI; Neuropathic pain; Psychology; Connectome; Medicine; Neuroscience; Physical medicine and rehabilitation; Computer science","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":[0.001797475,0.0004698472,0.0004414894,0.0008410733,0.0002477251,0.0007662161,0.0004631712,0.0004551172,0.001527854],"category_scores_gemma":[0.00664133,0.0002271237,0.0009248846,0.000648891,0.0006675991,0.0009292366,0.0006013259,0.0008498471,0.0001334186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007042066,"about_ca_system_score_gemma":0.0004148552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008978569,"about_ca_topic_score_gemma":0.008583996,"domain_scores_codex":[0.9995178,0.0002112557,0.00001699665,0.0001478573,0.00002937156,0.00007659067],"domain_scores_gemma":[0.9980438,0.001303867,0.0002723332,0.0001712138,0.000107061,0.0001017512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001139993,0.0004880328,0.4394061,0.0001616057,0.001217211,0.0006837338,0.0008829241,0.4318572,0.00969113,0.01415859,0.002325048,0.09798849],"study_design_scores_gemma":[0.00001265231,0.00009284541,0.103549,0.00001224785,0.00005208314,0.0001317444,0.00006262674,0.8849965,0.0002637462,0.0105681,0.0002379766,0.00002035429],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.933781,0.0003512676,0.06373414,0.0005168625,0.00001862905,0.00003591178,0.0004383813,0.0001315165,0.0009922538],"genre_scores_gemma":[0.9966462,0.00007869576,0.00275617,0.00002259689,0.00001010832,0.00001571048,0.0002089951,0.000009741564,0.000251777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008978569,"threshold_uncertainty_score":0.0178526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02768841337828585,"score_gpt":0.267904453422358,"score_spread":0.2402160400440722,"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."}}