Test Accuracy of Cognitive Screening Tests for Diagnosis of Dementia and Multidomain Cognitive Impairment in Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Guidelines recommend screening stroke-survivors for cognitive impairments. We sought to collate published data on test accuracy of cognitive screening tools. METHODS: Index test was any direct, cognitive screening assessment compared against reference standard diagnosis of (undifferentiated) multidomain cognitive impairment/dementia. We used a sensitive search statement to search multiple, cross-disciplinary databases from inception to January 2014. Titles, abstracts, and articles were screened by independent researchers. We described risk of bias using Quality Assessment of Diagnostic Accuracy Studies tool and reporting quality using Standards for Reporting of Diagnostic Accuracy guidance. Where data allowed, we pooled test accuracy using bivariate methods. RESULTS: From 19 182 titles, we reviewed 241 articles, 35 suitable for inclusion. There was substantial heterogeneity: 25 differing screening tests; differing stroke settings (acute stroke, n=11 articles), and reference standards used (neuropsychological battery, n=21 articles). One article was graded low risk of bias; common issues were case-control methodology (n=7 articles) and missing data (n=22). We pooled data for 4 tests at various screen positive thresholds: Addenbrooke's Cognitive Examination-Revised (<88/100): sensitivity 0.96, specificity 0.70 (2 studies); Mini Mental State Examination (<27/30): sensitivity 0.71, specificity 0.85 (12 studies); Montreal Cognitive Assessment (<26/30): sensitivity 0.95, specificity 0.45 (4 studies); MoCA (<22/30): sensitivity 0.84, specificity 0.78 (6 studies); Rotterdam-CAMCOG (<33/49): sensitivity 0.57, specificity 0.92 (2 studies). CONCLUSIONS: Commonly used cognitive screening tools have similar accuracy for detection of dementia/multidomain impairment with no clearly superior test and no evidence that screening tools with longer administration times perform better. MoCA at usual threshold offers short assessment time with high sensitivity but at cost of specificity; adapted cutoffs have improved specificity without sacrificing sensitivity. Our results must be interpreted in the context of modest study numbers: heterogeneity and potential bias.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.135 | 0.497 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.027 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".