Validation of the Turkish Version of the DDQ-30: Norms and Clinical Utility for Detecting Lexical-Semantic Impairments
Bibliographic record
Abstract
OBJECTIVE: This study adapted the Definition-based Naming Test (DDQ-30) into Turkish (DDQ-30 TR) and assessed its psychometric properties. METHOD: The adaptation process included translation, cultural and linguistic modifications, expert review, and pilot testing. Normative data were collected from 357 cognitively healthy Turkish adults aged 50-79. Known-group discriminant validity was examined in 150 participants, including healthy controls (n = 50), individuals with mild cognitive impairment (MCI; n = 60), and Alzheimer's disease (ad; n = 40). RESULTS: DDQ-30 TR performance declined with age and improved with education. Norms were stratified by age, education, and sex. The test showed strong group validity (HC > MCI > ad) and acceptable reliability (α = 0.758). CONCLUSIONS: The DDQ-30 TR is a reliable, culturally adapted auditory naming tool that enables lexical-semantic assessment in Turkish-speaking older adults, particularly those with visual limitations. The DDQ-30 TR can be used as a supportive instrument within broader clinical screening protocols for neurocognitive disorders, especially among individuals with visual limitations.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".