Normative data for the 12-item Buschke memory task in the Quebec-French population aged 50 and over
Bibliographic record
Abstract
OBJECTIVE: The 12-item Buschke memory test is used to assess verbal episodic memory in adults and older adults. However, there is no normative data for this test adjusted to the older Quebec-French population. The aim of the study was to produce normative data for the 12-item Buschke for the Quebec-French population aged 50 and older. METHOD: The normative sample consisted of 172 healthy French-speaking participants aged 50-89 years, from the Province of Quebec (Canada). The influence of age, years of formal education, and sex on five 12-item Buschke scores were analyzed. Based on the distribution of scores, normative data were developed as Z-scores equation, regression equation, and percentiles. RESULTS: Age, years of formal education, and sex were all associated with performance. Equations to calculate Z-scores were provided for the free recall trial 1 and the free recall trials 1-3. Stratified percentiles were provided for the delayed free recall and total recall 1-3. CONCLUSIONS: The normative data for the 12-item Buschke improve the accuracy of clinicians to detect verbal episodic memory impairments in Quebec's aging population.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".