Normative Data for the Alternating and Orthographic Constraint Semantic Fluency Tests in the Adult French-Quebec Population and Validation Study in Mild Cognitive Impairment and Alzheimer’s Disease
Bibliographic record
Abstract
Verbal fluency tests, known to elicit executive functions (EFs), have proven useful in distinguishing healthy individuals from those with cognitive impairment. The present study addresses two new tests of verbal fluency that elicit EFs, namely, extradimensional alternating fluency (EAF) and extradimensional orthographic constraint semantic fluency (EOCSF). The aim of Study 1 was to provide normative data in the adult and elderly population of French Québec for the two fluency tests. The aim of Study 2 was to determine their psychometric value. The normative sample consisted of 338 healthy controls (HCs) aged 50-89 years. Multiple linear regressions were used to generate equations for calculating Z-scores. Convergent validity was established by administering the two verbal fluency tests and the Letter-Number Sequence (LNS) subtest of the WAIS-III. To assess predictive validity, the performance of 19 HCs was compared with that of 19 participants with mild cognitive impairment (MCI) and 19 participants with Alzheimer's disease (AD). To determine test-retest reliability, the test was administered twice, 3 months apart, to a subsample of 20 HCs. Age and educational level were significantly related to performance in the EAF and the EOCSF. The two tests correlated significantly and positively with the LNS. The EAF and the EOCSF distinguished the performance of HCs from that of participants with MCI or AD. A test-retest analysis showed that scores on the two tests were stable over time. The norms and psychometric data for the EAF and the EOCSF will help clinicians and researchers better identify executive 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 | 0.005 |
| 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 | 0.000 |
| 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".