Construction of a short version of the Montreal Cognitive Assessment (MoCA) rating scale using Partial Least Squares analysis
Bibliographic record
Abstract
Abstract Background The Montreal Cognitive Assessment (MoCA) rating scale is frequently used to assess cognitive impairments in amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD). Objectives The aims of this study are to a) evaluate the construct validity of the MoCA and its subdomains or whether the MoCA can be improved by feature reduction, and b) develop a short version of the MoCA (MoCA-Brief). Methods We recruited 181 participants, divided into 60 healthy controls, 61 aMCI, and 60 AD patients. Results The construct reliability of the original MoCA was not optimal and could be improved by deleting one subdomain (Naming) and five items, namely Clock Circle, Lion, Digit Forward, Repeat 2 nd Sentence, and Place, which showed inadequate loadings on the extracted latent vectors. To construct the MoCA-Brief, the reduced model underwent further reduction and feature selection based on model quality data of the outer models. We produced a MoCA-Brief rating scale comprising five items, namely Clock Time, Subtract 7, Fluency, Month, and Year. The first latent vector extracted from these five indicators showed adequate construct validity with an Average Variance Extracted of 0.599, composite reliability of 0.822, Cronbach’s alpha of 0.832 and rho_A of 0.833. The MoCA-Brief factor score showed a strong correlation with the total MoCA score (r=0.98, p<0.001) and shows adequate concurrent, test-retest, and inter-rater validity. Conclusion The construct validity of the MoCA may be improved by deleting five items. The new MoCA-Brief rating scale deserves validation in independent samples and especially in other countries.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".