Normative data for phonemic and semantic verbal fluency test in the adult French–Quebec population and validation study in Alzheimer’s disease and depression
Bibliographic record
Abstract
Objective: Verbal fluency tasks are principally used to assess lexical access and have shown usefulness for differential diagnosis. The purpose of Study 1 was to provide normative data in the adult French–Quebec population (Canada) for semantic verbal fluency (animals), for two sets of phonemic verbal fluency (TNP and PFL), and for letter P alone (60 seconds per category/letter). The objectives of Study 2 were to establish the diagnostic and predictive validity of the present tasks and normative data in Alzheimer’s disease (AD) and major depressive episode (MDE). Method: The normative sample consisted of 932 participants aged 19–91 years. Based on multiple linear regressions, equations to calculate Z-scores were provided. To assess validity, performance of 62 healthy participants was compared to 62 participants with AD and 41 with MDE aged over 50. Results: Age and education, but not gender, predicted performance on each verbal fluency task. Healthy adults aged 50 and younger had a better performance on semantic than phonemic verbal fluency. In comparison to MDE, AD participants had lower performance on animals and TNP, but not on letter P. Ninety percent of people with a Z-score ≤ −1.50 on semantic verbal fluency had AD and the global accuracy was 76.6%. Test–retest reliability over one year was high for both animals (r = .711) and TNP (r = .790) in healthy older participants, but dropped for animals in people with AD (r = .493). Conclusions: These data will strengthen accurate detection of verbal fluency deficits in French–Quebec 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".