{"id":"W4210496898","doi":"10.1093/braincomms/fcac020","title":"Sex-specific lesion pattern of functional outcomes after stroke","year":2022,"lang":"en","type":"article","venue":"Brain Communications","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; Montreal Neurological Institute and Hospital; McGill University; University of British Columbia","funders":"National Institute of Neurological Disorders and Stroke; National Institute on Aging; Canadian Institutes of Health Research; Vetenskapsrådet; National Institutes of Health; Agence Nationale de la Recherche; Fondation Brain Canada","keywords":"Stroke (engine); Medicine; Posterior cingulate; Neuroimaging; Lesion; Precuneus; Cardiology; Internal medicine; Radiology; Functional magnetic resonance imaging; Pathology; Psychiatry","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":[0.001596271,0.0003515839,0.0003071413,0.0005534492,0.0001884288,0.0005544453,0.0002941904,0.0002727882,0.002510887],"category_scores_gemma":[0.006413865,0.0001164971,0.0005943913,0.0003831721,0.0002983112,0.0003191939,0.0004431473,0.0003587592,0.0003594703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001348105,"about_ca_system_score_gemma":0.0002388232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004034556,"about_ca_topic_score_gemma":0.004097952,"domain_scores_codex":[0.9994369,0.0002240974,0.00003895674,0.0001899867,0.00005842954,0.00005166529],"domain_scores_gemma":[0.9983438,0.0006527431,0.0004652753,0.0003009331,0.0001315847,0.0001056597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002186238,0.00001372294,0.9910451,0.00001359577,0.0002209679,0.00007544895,0.0001650366,0.0005845108,0.0006605914,0.0001703329,0.0001113756,0.006720653],"study_design_scores_gemma":[0.000008307959,0.00008997155,0.9933988,0.00001134613,0.00008078993,0.0003404979,0.0001371352,0.004103839,0.0002251417,0.001292948,0.0003022395,0.00000899166],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996363,0.0003616645,0.001929086,0.00008382761,0.000008271752,0.00001013505,0.0005946,0.00001469638,0.0006347286],"genre_scores_gemma":[0.999326,0.00005970856,0.0001769756,0.00001026964,0.000004885679,0.000003156336,0.0003014831,0.000005404989,0.0001122326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004034556,"threshold_uncertainty_score":0.008441985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05704357829612738,"score_gpt":0.2989643369007581,"score_spread":0.2419207586046307,"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."}}