Compact MoCA: A shortened alternative for MCI screening among healthy aging individuals
Bibliographic record
Abstract
Abstract Background The increasing number of elderlies highlights the important of clinical screening for cognitive impairment in healthcare setting. Montreal Cognitive Assessment (MoCA) is a widely used tool for mild cognitive impairment (MCI) screening, particularly among resource‐limited settings. Although originally claims to be a time‐saving tool of approximately 10 minutes, it takes much longer in real‐world settings, which consequently led to lack of MCI screening in many busy clinic settings. In order to shorten the duration for testing, we aimed to determine the performance of a compact MoCA which excludes domain(s) that does not correlate well with the total score. Method Data of Thai healthy adults aged ≥60 years from a prospective cohort conducted in King Chulalongkorn Memorial Hospital, a tertiary care center in Bangkok, Thailand was included. MoCA was used to assess cognitive performance. Cognitive domain that showed poor correlation to the total score was excluded to build a compact MoCA in order to shorten the time of assessment. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were used to evaluate the performance of the compact MoCA. Result A total of 2,799 participants who completed MoCA were enrolled. Correlation analysis showed that naming and orientation had the lowest degree of correlation to the total MoCA score (coefficients of 0.355 and 0.364, respectively) and delayed recall had the highest degree of correlation (coefficients of 0.609). After excluding naming, MoCA showed 100% sensitivity and 96.05% at a cut‐off score of 25. After excluding orientation, MoCA showed 100% sensitivity and 97.60% specificity at a cut‐off score of 19. After excluding both naming and orientation, MoCA showed 100% sensitivity and 93.4% specificity at a cutoff score 16. AUROC of MoCA after excluding naming, orientation, and both naming and orientation was 0.996, 0.998 and 0.994, respectively. Conclusion The compact MoCA which excludes either naming or orientation showed great performance with 100% sensitivity and >95% specificity, suggesting a possible alternative method to shorten the duration for MCI screening while maintaining the quality of the test. An on‐going MCI screening using an electronic MoCA is on‐going to precisely assess time use per each domain.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".