Which cutoff value of the Montreal Cognitive Assessment should be used for post-stroke cognitive impairment? A systematic review and meta-analysis on diagnostic test accuracy
Bibliographic record
Abstract
Background:Post-stroke cognitive impairment (PSCI) is one of the serious complications of stroke. The Montreal Cognitive Assessment (MoCA), as a brief cognitive impairment screening tool, is widely used in stroke survivors. However, some studies have suggested that the use of the universal cutoff value of 26 may be inappropriate for detecting cognitive impairments in stroke settings.Aim:We conducted this study to identify the optimal cutoff value of the MoCA in screening for PSCI.Methods:PubMed, CINAHL, Embase, the Cochrane Library, and Web of Science were searched for eligible studies until March 23, 2023. All studies were screened by two independent researchers. The quality of each article was evaluated by the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate mixed-effects model was used to pool sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and the summary receiver operating characteristic curve.Results:Twenty-four studies with a total of 4231 patients were included in this review. Despite the lack of evidence of publication bias, a high degree of heterogeneity was observed. A meta-analysis revealed that a cutoff value of 21/22 yielded the best diagnostic accuracy. The optimal cutoff varied in different regions, stroke types, and stroke phases as well.Conclusion:The optimal cutoff of MoCA was 21/22 for stroke populations rather than the initially recommended cutoff of 26. A revised (lower) cutoff should be considered for stroke survivors.
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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 | 0.040 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.039 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".