{"id":"W2076445422","doi":"10.1159/000369883","title":"Optimising the Cutoffs of Cognitive Screening Instruments in Pragmatic Diagnostic Accuracy Studies: Maximising Accuracy or the Youden Index?","year":2015,"lang":"en","type":"article","venue":"Dementia and Geriatric Cognitive Disorders","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Youden's J statistic; Diagnostic accuracy; Montreal Cognitive Assessment; Index (typography); Cognition; Test (biology); Cognitive impairment; Psychology; Mathematics; Statistics; Medicine; Receiver operating characteristic; Internal medicine; Computer science; Psychiatry","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001890088,0.0003901971,0.0005679178,0.0004126408,0.0004000141,0.0001467518,0.0002414075,0.0000917584,0.0001305004],"category_scores_gemma":[0.02007564,0.0002117223,0.0001399915,0.001387339,0.0005975398,0.0004330634,0.0005043624,0.0004712631,0.00001206421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005828021,"about_ca_system_score_gemma":0.0003670907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002821424,"about_ca_topic_score_gemma":0.0002549156,"domain_scores_codex":[0.9963931,0.0005509985,0.0007686511,0.0005346868,0.001003833,0.000748734],"domain_scores_gemma":[0.9904019,0.007862638,0.0004470267,0.0002333579,0.0008375223,0.0002175394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001908373,0.0006316986,0.6609247,0.0002915031,0.00178168,0.0000594257,0.01141032,0.00001149682,0.00002521836,0.00002162949,0.0001856234,0.3227484],"study_design_scores_gemma":[0.01733166,0.001282374,0.7576995,0.002318811,0.00249843,0.00006700974,0.2155376,0.001160712,0.0003067804,0.000957129,0.000362041,0.0004780358],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985363,0.004901382,0.003072011,0.001753173,0.0001500504,0.003305019,0.00003617306,0.00003086847,0.001388321],"genre_scores_gemma":[0.9950968,0.003530402,0.0001121392,0.0006088672,0.00007427705,0.0003304143,0.00007630447,0.00003850742,0.0001322874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3222703,"threshold_uncertainty_score":0.9881787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04114307652231406,"score_gpt":0.3606821490292886,"score_spread":0.3195390725069745,"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."}}