Validation of Montreal Cognitive Assessment, MoCA, Alternate French Versions
Bibliographic record
Abstract
UNLABELLED: Objective background: The Montreal Cognitive Assessment (MoCA) is a questionnaire that has been developed to help physicians around the world diagnose a patient's cognitive ability. Available in multiple languages and for use in multiple countries worldwide, the goal of this study was to validate the alternate versions 2 and 3 of the French MoCA test to assist physicians in the detection of mild cognitive impairment (MCI), while decreasing the learning effect upon frequent testing. METHODS: A validation study was conducted at the MoCA Clinic and Institute in Québec, Canada. The subject population consisted of 25 patients diagnosed with MCI meeting Petersen criteria and 25 healthy subjects serving as the normal control (NC) group. Three MoCA test versions were administered in the French language in random order within one session. Scores obtained in all three versions in MCI and NC groups were assessed for reliability and consistency from one version to the next. RESULTS: On average, scores obtained in each subject group (MCI and NC) fell within their corresponding diagnostic ranges (score above 26 points for NC patients versus scores below 26 points for MCI patients). Difference in scores observed between the original French MoCA version and the two alternate versions in each subject cohort were minimal and not considered clinically significant. CONCLUSIONS: All three test versions of the French MoCA are considered equivalent in diagnostic reliability and consistency and contribute to decreasing the potential learning effect when patients are required to repeat the test frequently.
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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.002 | 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".