Trail Making Tests A and B: regression-based normative data for Quebec French-speaking mid and older aged adults
Bibliographic record
Abstract
Objective: The Trail Making Test (TMT) is mainly used to assess visual scanning/processing speed (part A) and executive functions (part B). The test has proven sensitive at detecting cognitive impairment during aging. However, previous studies have shown differences between normative data from different countries and cultures, even when corrected for age and education. Such inconsistencies between normative data may lead to serious diagnostic errors, thus, the development of local norms is warranted. The purpose of this study was to provide regression-based normative data for TMT-A and -B, tailored for a large sample of French-speaking adults from Quebec (Canada). Method: The normative sample consisted of 792 participants aged 50–91 years. Based on multiple linear regression, equations to calculate Z-scores were provided for TMT-A and -B, and for a contrast score which compared performance between TMT-A and -B. Percentiles, stratified by age, are presented for the number of recorded errors. Results: Age was a significant predictor for TMT-A performance, while age and education were independently associated with performance on TMT-B. Gender did not have any effect on performance, in either condition. Education was the only significant predictor of the contrast score between TMT-B and TMT-A. Examiners should remain vigilant when two or more errors are recorded on the TMT-B since this was uncommon in the normative sample. Conclusions: Our TMT normative data improve the accurate detection of visual scanning/processing speed and executive function deficits in Quebec (Canada) French-speaking 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 | 0.002 | 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 | 0.000 | 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".