Screening for post-stroke neurocognitive disorders in diverse populations: A systematic review
Bibliographic record
Abstract
Objective: Although neurocognitive disorders (NCD) are common post-stroke, many populations do not have adapted cognitive screens and cut-offs. We therefore reviewed the appropriateness of the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Oxford Cognitive Screen (OCS) for diagnosing NCD in culturally diverse stroke populations. Method: Using an extensive search string, diagnostic accuracy studies for MMSE, MoCA and OCS in the stroke population were retrieved from four databases. We compared translations and adaptations, adjustments in scores and cut-offs, and their diagnostic accuracy. Results: The search resulted in 28 MMSE, 39 MoCA and 5 OCS-studies in 13 western, educated, industrialized, rich and democratic (WEIRD) and 4 other countries. There was a lack of studies on South-American, African, and non-Chinese-Asian populations. All three tests needed adaptation for less WEIRD populations and populations with languages with non-Latin features. Optimal MMSE and OCS subtest cut-offs were similar across WEIRD and less WEIRD populations, whereas optimal MoCA cut-offs appeared lower for less WEIRD populations. The use of adjusted scores resulted in different optimal cut-offs or similar cut-offs with better accuracy. Conclusions: MoCA, MMSE and OCS are promising tools for diagnosing post-stroke-NCD. For culturally diverse populations, translation, adaptation and adjusted scores or cut-offs are necessary for diagnostic accuracy. Available studies report scarcely about their sample’s cultural background and there is a lack of diagnostic accuracy studies in less WEIRD or culturally diverse populations. Future studies should report more cultural characteristics of their sample to provide better insight into the tests’ accuracy in culturally diverse populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".