87CAN THE MONTREAL COGNITIVE ASSESSMENT BE SHORTENED?
Bibliographic record
Abstract
Background: A short form Montreal cognitive assessment (SF-MoCA) may have many potential benefits including reduced test burden. Methods: We sought to identify all SF-MoCA versions described in the literature, and analysed their accuracy in two datasets, one specific to stroke and another from a memory clinic (Walton Centre, Liverpool). We used Spearman correlation coefficient and principal component analysis to identify potentially redundant items in the MoCA. We then compared the sensitivity, specificity and negative/positive predictive values (NPV/PPV) of each SF-MOCA against a mini-mental state examination (MMSE) score of <24 (in stroke) and a diagnosis of dementia (in memory clinic). Results: We screened 579 titles and identified 13 distinct SF-MoCAs, ten of which included enough information for our quantitative analysis. We analysed data from 787 stroke patients with a median age of 70, median NIHSS of 4 and median MoCA of 21; and 410 patients from the memory clinic, with median age of 60 and median MoCA of 23. Spearman correlation coefficient and principal components analysis, suggested no particular item was more redundant Test accuracy of the various SF-MoCA varied (Table) SF MoCA described by Cecato et al (Clock, animal naming, delayed recall and orientation) had the most favourable balance of NPV and PPV. Table Test accuracy of various SF-MoCAs Conclusion: There are many SF-MoCA versions described, with differing test properties, some of may have clinical utility. If using an abbreviated MoCA, clinicians and researchers should state the items tested. Funding: This work was funded by a BGS Start-up Grant
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".