Validation of and Normative Data of the DVAQ-30, a New Video-Naming Test for Assessing Verb Anomia
Bibliographic record
Abstract
OBJECTIVE: Anomia is usually assessed using picture-naming tests. While many tests evaluate anomia for nouns, very few tests have been specifically designed for verb anomia. This article presents the DVAQ-30, a new naming test for detecting verb anomia in adults and elderly people. METHOD: The article describes three studies. Study 1 focused on the DVAQ-30 development phase. In Study 2, healthy participants and individuals with post-stroke aphasia, mild cognitive impairment, Alzheimer's disease, or primary progressive aphasia were assessed using the DVAQ-30 to establish its convergent and discriminant validity, test-retest reliability, and internal consistency. In Study 3, a group of adults and elderly Quebec French-speaking adults were assessed to obtain normative data. RESULTS: The DVAQ-30 had good convergent validity and distinguished the performance of healthy participants from that of participants with pathological conditions. The test also had good internal consistency, and the test-retest analysis showed that the scores had good temporal stability. Furthermore, normative data were collected on the performance of 244 participants aged 50 years old and over. CONCLUSIONS: The DVAQ-30 fills an important gap and has the potential to help clinicians and researchers better detect verb anomia associated with pathological aging and post-stroke aphasia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".