Turkish adaptation, reliability, and validity of the detection test for language impairments in adults and the aged
Bibliographic record
Abstract
There is no quick, valid, and reliable screening tool in Turkish that can be used for screening language disorders associated with major neurocognitive disorders (MND). To fill this gap, we designed three distinct studies. In the first study, we adapted the Detection Test for Language Impairments in Adults and the Aged into the Turkish language (DTLA-Tr). In the second study, we collected data from 175 Turkish individuals to determine the normative data of the DTLA-Tr. In the last study, we investigated the psychometric properties of the DLTA-Tr by comparing 17 healthy individuals with 17 patients with Alzheimer's disease and determining its test-retest reliability. As a result of Study 1, the DTLA was adapted to the Turkish adult population. In Study 2, the normative data of the DTLA-Tr were provided. The results of this study indicated a positive correlation between educational level and DTLA-Tr total score. The results of Study 3 showed that the DTLA-Tr has high predictive validity and good test-retest reliability. The DTLA-Tr is a valid and reliable tool for assessing language abilities in both adults and the elderly. The findings of this study have significant implications for the evaluation of language in Turkish-speaking patients with MND.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".