Normative data for the Tower of London (Drexel version) in the Quebec-French population aged between 50 and 88 years
Bibliographic record
Abstract
The Tower of London (ToL) is a neuropsychological test used to assess several executive functions such as strategical reasoning, mental planning, and problem-solving. Like other cognitive tests, performance on the ToL can vary according to age, level of education, sex, and cultural background of individuals. The present study aimed to establish normative data for the Drexel version of the ToL among French-Quebec people aged 50 years and over. The normative sample consisted of 174 healthy individuals aged 50-88 years, all from the province of Quebec, Canada. Analyses were performed to estimate the associations between age, sex, and education level on one hand, and ToL performance, on the other hand. Results indicated that Total Execution Time was associated with age, whereas the Total Type II Errors and Total Rule Violation score (Type I + II Errors) were associated with both age and education level. All other scores were not significantly associated with the demographic characteristics of the participants. Since the distributions of the data were all skewed, the normative data are presented in the form of percentile ranks. To conclude, the present norms will ease the detection of executive impairments in French-Quebec middle-aged and older adults.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".