Normative data for the Color Trails Test in middle-aged and elderly Quebec-French people
Bibliographic record
Abstract
Despite the widespread use of the Color Trails Test (CTT) in clinical and research settings, information regarding the impact of sociodemographic variables on test performance in Quebec-French adults and elderly people is non-existent. This study aimed to establish French-Quebec normative data for error scores and completion time on all test trials (CTT1 and CTT2) taking into account the impact of age, education, and sex on test performance. The sample consisted of 169 community-dwelling and healthy Quebec-French individuals aged between 50 and 90 years and having between 6 and 21 years of formal education. Regression analyses indicated that age was associated with completion time on CTT1 and CTT2. Spearman correlations also revealed that age was positively associated with error scores (CTT1 errors, CTT2 number errors, CTT2 near-misses) and index interference. Education was marginally associated with CTT1 but was not associated with CTT2 completion time or interference index. Education was only associated with the number of errors in the CTT2. Finally, sex was not associated with any variables. Equations to calculate Z scores and percentiles are presented. Norms for the CTT will ease the interpretation of executive functioning in Quebec-French adults and the elderly and favor accurate discrimination between normal and pathological cognitive states.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".