Montreal Cognitive Assessment
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The objective of this study is to examine the discrepancy between single versus age and education corrected cutoff scores in classifying performance on the Montreal Cognitive Assessment (MoCA) in patients with stroke or transient ischemic attack. METHODS: MoCA norms were collected from 794 functionally independent and stroke- and dementia-free persons aged ≥65 years. magnetic resonance imaging was used to exclude healthy controls with significant brain pathology and medial temporal lobe atrophy. Cutoff scores at 16th, 7th, and 2nd percentiles by age and education were derived for the MoCA and MoCA 5-minute Protocol. MoCA performance in 919 patients with stroke or transient ischemic attack was classified using the single and norm-derived cutoff scores. RESULTS: The norms for the Hong Kong version of the MoCA total and domain scores and the total score of the MoCA 5-minute protocol are described. Only 65.1% and 25.7% healthy controls and 45.2% and 19.0% patients scored above the conventional cutoff scores of 21/22 and 25/26 on the MoCA. Using classification with norm-derived cutoff scores as reference, locally derived cutoff score of 21/22 yielded a classification discrepancy of ≤42.4%. Discrepancy increased with higher age and lower education level, with the majority being false positives by single cutoffs. With the 25/26 cutoff of the original MoCA, discrepancy further increased to ≤74.3%. CONCLUSIONS: Conventional single cutoff scores are associated with substantially high rates of misclassification especially in older and less-educated patients with stroke. These results caution against the use of one-size-fits-all cutoffs on the MoCA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.020 |
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".