{"id":"W3029462569","doi":"10.3760/cma.j.issn.1674-6554.2018.08.007","title":"Prediction of white matter lesions and subcortical atrophy for cognitive impairment in patients with ischemic stroke","year":2018,"lang":"en","type":"article","venue":"Zhonghua xingwei yixue yu naokexue zazhi","topic":"Neurological Disease Mechanisms and Treatments","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Internal medicine; Montreal Cognitive Assessment; Medicine; Cardiology; Atrophy; Stroke (engine); Atrial fibrillation; Leukoaraiosis; Psychology; Cognitive impairment; Dementia; Disease","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005251454,0.0005383526,0.000370193,0.001142981,0.0004465326,0.0006668653,0.0003322807,0.0005804819,0.001637825],"category_scores_gemma":[0.002170348,0.0001941913,0.0004809386,0.0007750213,0.0002419766,0.0004945054,0.0004045638,0.0006610249,0.0003720896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001686798,"about_ca_system_score_gemma":0.000363662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002355437,"about_ca_topic_score_gemma":0.003919696,"domain_scores_codex":[0.9997334,0.00004938995,0.00004249085,0.00005312987,0.00006840369,0.00005315349],"domain_scores_gemma":[0.9991711,0.0001373305,0.0003409305,0.00002923332,0.0001095624,0.0002118859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008754918,0.00003304949,0.9987729,0.00000606287,0.00002424609,0.00008545294,0.00001605644,0.00003954729,0.00005156355,0.000005293852,0.00004652447,0.0008317258],"study_design_scores_gemma":[0.000006831936,0.0001173754,0.9988424,0.000007169757,0.00003551551,0.0003370969,0.0001094663,0.000357618,0.00004162873,0.00003762542,0.0001038513,0.000003493274],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998149,0.00058357,0.00008815052,0.0001043753,0.00001845298,0.00001185538,0.0002124641,0.000005116649,0.000827074],"genre_scores_gemma":[0.9992276,0.0001771333,0.00007901675,0.00002152897,0.00003452968,0.000006090323,0.0002207819,0.000001064674,0.0002321296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002355437,"threshold_uncertainty_score":0.005479038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01846816078762078,"score_gpt":0.2314347670013125,"score_spread":0.2129666062136917,"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."}}