RAVLT and Nonverbal Analog: French Forms and Clinical Findings
Bibliographic record
Abstract
BACKGROUND: Objective clinical evaluation of memory frequently requires serial testing but the issue of whether multi-formed tests are equivalent and can be used interchangeably is seldom examined. An added problem in bilingual Canadian settings is the extent to which it is appropriate to measure French speakers' performance on translations of English tests. The present work used the Rey Auditory Verbal Learning Test (RAVLT) and a nonverbal analog, the Aggie Figures Learning Test (AFLT), to examine whether a) different forms of the same test are equivalent, b) performance on the two tests is comparable, c) two language groups perform similarly, and d) the RAVLT can detect dysfunction in patients with temporal lobe epilepsy (TLE). METHODS: We compared three French versions of the RAVLT and three forms of the AFLT in 114 healthy francophone adults. We subsequently compared the performance of the same francophone subjects to a previously obtained sample of anglophones on both tests, and then administered the RAVLT to anglophone or francophone patients with TLE. RESULTS: For both tasks the three forms were equivalent and performance on the RAVLT was comparable to that on the AFLT. Francophone subjects performed slightly worse on the RAVLT compared to anglophones but performance of the two language groups did not differ on the AFLT. Finally, left TLE patients were impaired compared to right on the RAVLT, but no performance differences were observed across the two language groups in the patient sample. CONCLUSIONS: The RAVLT and AFLT are useful tools for examination of learning and memory in French and English speaking populations. On the RAVLT, the lesion effect in patients is not affected by differences in performance between language groups.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".