{"id":"W3126906418","doi":"10.1097/tld.0000000000000242","title":"Predicting Cognitive Impairment in Cerebrovascular Disease Using Spoken Discourse Production","year":2021,"lang":"en","type":"article","venue":"Topics in Language Disorders","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Psychology; Dementia; Neuropsychology; Cognition; Discriminant function analysis; Observational study; Linear discriminant analysis; Spoken language; Neuropsychological assessment; Set (abstract data type); Disease; Developmental psychology; Audiology; Cognitive psychology; Artificial intelligence; Psychiatry; Medicine; Statistics; Computer science; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002840716,0.0001351835,0.0001921976,0.0001885908,0.00004733543,0.00002338807,0.00004889236,0.00004434613,0.000423205],"category_scores_gemma":[0.000442663,0.0001330698,0.00009094441,0.0004371557,0.00007793578,0.0001271349,0.00007817877,0.0002590343,0.000009090428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001420994,"about_ca_system_score_gemma":0.0002230338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003347862,"about_ca_topic_score_gemma":0.0008010543,"domain_scores_codex":[0.9985406,0.0001199041,0.0002266806,0.0003846186,0.0003660909,0.0003621422],"domain_scores_gemma":[0.9995438,0.00002571548,0.0000355975,0.000202333,0.00007534276,0.0001172349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001394368,0.000846295,0.9844284,0.0001992269,0.00004780577,0.0004852046,0.00249504,0.00002807272,0.0003343123,0.00002484618,0.000007496175,0.01096387],"study_design_scores_gemma":[0.002748326,0.00008263584,0.9683557,0.0005808608,0.00009905892,0.00001626836,0.02526748,0.0009779072,0.001499261,0.0001661697,0.00004453563,0.0001617721],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951026,0.0009169223,0.000144639,0.001121402,0.000132727,0.0007407807,0.000007787947,0.00002779437,0.001805364],"genre_scores_gemma":[0.9979644,0.0001461178,0.0001986374,0.0001446346,0.0001468898,0.00005840451,0.0001052381,0.00001935486,0.001216302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02277244,"threshold_uncertainty_score":0.5426427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01321160757684584,"score_gpt":0.3366998140147143,"score_spread":0.3234882064378685,"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."}}