Normalisation du <i>Mini-Mental State Examination</i> (MMSE) chez les Québécois francophones âgés de 65 ans et plus et résidant dans la communauté
Bibliographic record
Abstract
ABSTRACTThis study was aimed at providing normative data for the Mini-Mental State Examination (MMSE). The norms were built from a sample (n = 2409) of community-dwelling French speaking residents from Québec aged 65 and older. The analyses indicated that socio-demographic variables such as education level, age, and gender of individuals influenced significantly the scores of older adults on the MMSE. More precisely, MMSE scores increased with education level and decreased with age. Moreover, women had significantly higher scores than men. On this basis, distinct tables of normative data were produced for women and men. In each table, the MMSE scores corresponding to percentiles 5, 10, 15 and 50 were identified according to four age categories and three education levels. Overall, the use of the present normative data by clinicians will improve their accuracy in detecting cognitive impairment in older adults from Québec.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".