TDQ-30—A New Color Picture-Naming Test for the Diagnostic of Mild Anomia: Validation and Normative Data in Quebec French Adults and Elderly
Bibliographic record
Abstract
OBJECTIVE: A reduction in lexical access is observed in normal aging and a few studies also showed that this ability is affected in individuals with subjective cognitive decline. Lexical access is also affected very early in mild cognitive impairment as well as in major neurocognitive disorders. The detection of word-finding difficulties in the earliest stages of pathological aging is particularly difficult because symptoms are often subtle or mild. Therefore, mild anomia is underdiagnosed, mainly due to the lack of sensitivity of naming tests. In this article, we present the TDQ-30, a new picture-naming test designed to detect mild word-finding deficits in adults and elderly people. METHOD: The article comprises three studies aiming at the development of the test (Study 1), the establishment of its validity and reliability (Study 2), and finally, the production of normative data for French-speaking adults and elderly people from Quebec (Study 3). RESULTS: The results showed that the TDQ-30 has good convergent validity. Also, the TDQ-30 distinguished the performance of healthy controls from those of participants with mild cognitive impairment, Alzheimer's disease, and post-stroke aphasia. This suggests good discriminant validity. Finally, this study provides normative data computed from a study sample composed of 227 participants aged 50 years and over. CONCLUSIONS: The TDQ-30 has the potential to become a valuable picture-naming test for the diagnosis of mild anomia associated with pathological aging.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".