Normative data for the verb fluency test in the adult French-Quebec population and validation study in mild cognitive impairment
Bibliographic record
Abstract
Verbal fluency tests are used to assess executive functions and language. The verb fluency test has proven successful in distinguishing healthy individuals (HCs) from participants with pathological conditions. However, few normative and psychometric studies have been published for the verb fluency test. The aim of Study 1 was to provide normative data in the adult population of French Québec for the verb fluency test. The aim of Study 2 was to determine its discriminant validity and test-retest reliability. The normative sample consisted of 424 HCs aged 50-92 years. Multiple linear regressions were used to generate equations for calculating Z-scores. To assess discriminant validity, the performance of 46 HCs was compared with that of 46 participants with mild cognitive impairment (MCI). To determine test-retest reliability, the test was administered twice, 3 months apart, to a group of 25 HCs. Age, sex, and education level were significantly related to performance on the test. The test distinguished the performance of HCs from that of participants with MCI. Test-retest analysis showed that scores had good stability over time. Norms and psychometric data for the verb fluency test will help clinicians and researchers better identify executive and language impairments associated with pathological conditions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".