{"id":"W4322616811","doi":"10.1002/hbm.26243","title":"Prediction of suicidality in bipolar disorder using variability of intrinsic brain activity and machine learning","year":2023,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute on Alcohol Abuse and Alcoholism; National High-tech Research and Development Program; Jiangsu Provincial Key Research and Development Program; National Natural Science Foundation of China; Jiangsu Commission of Health","keywords":"Neuroimaging; Bipolar disorder; Psychology; Anterior cingulate cortex; Precuneus; Functional magnetic resonance imaging; Resting state fMRI; Suicidal ideation; Brain activity and meditation; Major depressive disorder; Default mode network; Persistent vegetative state; Poison control; Neuroscience; Electroencephalography; Medicine; Injury prevention; Cognition; Consciousness; Minimally conscious state","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00261588,0.0001636005,0.0003576359,0.0004257234,0.000326315,0.00001634896,0.0001153753,0.00007735643,0.00001881757],"category_scores_gemma":[0.02046005,0.0001792145,0.00006166934,0.001123114,0.0003227475,0.0002618512,0.0003130278,0.0003448689,0.000001213634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008843939,"about_ca_system_score_gemma":0.00003756442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009482452,"about_ca_topic_score_gemma":0.0002542837,"domain_scores_codex":[0.9975815,0.0009246386,0.0003804606,0.0005418612,0.0003031338,0.0002683752],"domain_scores_gemma":[0.992114,0.007329997,0.0002316484,0.0002390612,0.00004842836,0.00003683221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00002061857,0.00006575037,0.323031,0.0001736958,0.00000874615,0.000001901332,0.001138419,0.000883689,0.6723587,0.0009296091,0.00002157851,0.001366301],"study_design_scores_gemma":[0.0005496506,0.000082912,0.9713828,0.00007756814,0.000005874418,0.000003712606,0.0002264832,0.01737613,0.006652386,0.002979164,0.0005321214,0.0001312096],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959546,0.0000460362,0.001975009,0.001391744,0.0000921456,0.0002960578,0.00003859604,0.0001103954,0.00009546377],"genre_scores_gemma":[0.99968,0.00001089669,0.00005357092,0.0001305854,0.00003575209,0.00001015986,0.000004576259,0.0000172008,0.00005727479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6657063,"threshold_uncertainty_score":0.987791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0968209140416311,"score_gpt":0.2907367412414136,"score_spread":0.1939158271997825,"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."}}