Population‐Based Norms for the Montreal Cognitive Assessment in Arab Adults
Bibliographic record
Abstract
OBJECTIVE: The Montreal Cognitive Assessment (MoCA) is a brief screening instrument for detecting mild cognitive dysfunction, a precursor to many cognitive disorders, such as dementia, which have increased in prevalence globally. Qatar, a small high-income country, has the largest projected increase in dementia of any country in the Middle East. Yet no population-based norms for cognitive function are available to date. METHODS: As part of the first national cross-sectional study of mental health, a total of 395 Qatari and non-Qatari Arabs, 18-74 years of age, were evaluated face-to-face using the Arabic version of the original MoCA (version 7.1). We computed raw and demographically (gender, age in years, and four education categories) adjusted scores for the overall MoCA test and six domains (visuospatial, executive function, attention, language, delayed memory, and orientation). The percentile ranking of raw and adjusted normative (z) scores was computed. The 5th percentile ranking was used to derive potential cut-offs for the overall test and the six related domains. RESULTS: Female gender, older age, and lower levels of education were associated with poorer overall test scores. The following MoCA overall test and domains cut-off scores (rounded to the nearest integer) were identified: MoCA (22), visuospatial (2), executive (2.5), attention (4), language (4), and delayed memory (3). CONCLUSIONS: On the basis of our population-based data, scores below these 5th percentile cut-offs may warrant further testing and clinical follow-up for mild cognitive impairment (MCI) in otherwise healthy Arab adults.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".