Normative Data for the Adult Turkish Population and Validation Study in Mild Cognitive Impairment and Alzheimer's Disease of the TDQ‐30 Tr, a Color Picture‐naming Test for Adults and the Elderly
Bibliographic record
Abstract
BACKGROUND: Individuals with mild anomia often have difficulty finding words during conversations, even when they get normal results on standard language evaluations. The high frequency and familiarity of target objects may lead to the insensitivity of some naming assessments, complicating diagnosis. This study aims to adapt the Test de dénomination de Québec-30 images (TDQ-30) into Turkish, develop normative data adapted to the Turkish population, and determine its validity in Turkish-speaking patients. METHODS: Data were collected from a total of 464 participants (414 healthy controls, 25 with Alzheimer's disease (AD), and 25 with mild cognitive impairment (MCI)) by using the Montreal Cognitive Assessment (MoCA), Boston Naming Test (BNT), Detection Test for Language Impairments in Adults and the Aged-Turkish version (DTLA-Tr), and Test de dénomination de Québec-30-Turkish version (TDQ-30 Tr). RESULTS: In Study 1, TDQ-30 was translated into Turkish and checked for cultural appropriateness for the Turkish community. In study 2, the adapted version, TDQ-30 tr, was applied to 414 healthy adults and elderly individuals to establish normative data for the test. In study 3, the analyses made known the validity of TDQ-30 Tr, and it was found that TDQ-30 can differentiate between healthy participants and those with AD and MCI, with healthy participants showing better results. CONCLUSION: In summary, the TDQ-30 Tr is an effective and dependable tool for identifying mild anomia associated with neurological impairments in Turkish-speaking adults and the elderly. Its ease of use makes it a vital instrument for improving evaluation tools in research and clinical settings in Turkey.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".