Normative Data for the Modified Location Learning Test (m-LLT) in the French–Quebec Population Aged Between 50 and 89 Years
Bibliographic record
Abstract
OBJECTIVE: This study aims to establish normative data for the modified Location Learning Test (m-LLT), considering sociodemographic characteristics such as age, sex, and educational level. MATERIALS AND METHODS: One hundred eighty-nine middle-aged and elderly people aged 50 years and older were recruited from the French-speaking population in Quebec (Canada). The m-LLT procedure described by Kessels et al. (2006) was used. Percentiles were derived for performance scores (Trial 1, Total Displacement Score, Learning Index, Delayed Recall Displacements), stratified by sociodemographic characteristics where appropriate. RESULTS: Regarding the sex variable, the number of displacements in Trial 1 and for the Total Displacement Score were higher in men than in women. Age was positively associated with the Total Displacement Score and Delayed Recall Displacements and negatively associated with the Learning Index. Education was positively associated with the Learning Index and Delayed Recall Displacements. Two-thirds of the normative sample achieved a perfect score on the fifth and final learning trial. CONCLUSIONS: Learning was better in women than in men, which may be explained by the use of verbal and nonverbal strategies and environmental awareness favoring women. The decline in learning and retrieval with age can be explained, among other reasons, by a less strategic approach during the encoding phase, a decline in other cognitive domains, or poorer imagery-based representations of the stimuli. The associations between education, strategic retrieval, and cognitive reserve are discussed. Overall, these normative data will enhance the detection of cognitive decline in geriatric clinical or research settings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.003 | 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".