The DDQ-30, a new naming-from-definition test: Normative data for the adult French-Quebec population and validation study in mild cognitive impairment and Alzheimer’s disease
Bibliographic record
Abstract
Assessing naming abilities is a standard clinical procedure for adults and is usually carried out using picture naming tests. However, the use of picture naming tests can have limitations, as people may have visual impairments that can affect the validity of the measurement. This article introduces the DDQ-30, a new naming-from-definition test for detecting anomia in people with visual-perceptual limitations. The article describes three studies. Study 1 focused on the developmental phase of the DDQ-30. In Study 2, healthy participants and individuals with mild cognitive impairment or Alzheimer's disease were assessed with the DDQ-30 to determine its predictive validity. Study 3 examined a group of adults and older French-speaking Quebecers to obtain normative data. The DDQ-30 effectively differentiated between AD and healthy participants. In addition, normative data were collected on 251 participants aged 50 years and older. Analyses showed that age and educational level were significantly related to performance on the DDQ-30. The DDQ-30 fills an important gap and promises to help clinicians and researchers better detect anomia in people with visual impairment.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".