Detection Test for Language Impairments in Adults and the Aged—A New Screening Test for Language Impairment Associated With Neurodegenerative Diseases: Validation and Normative Data
Bibliographic record
Abstract
To date, there is no quick screening test that could be used during routine office visits to accurately assess language disorders in neurodegenerative diseases. To fill this important gap, we developed the Detection Test for Language impairments in Adults and the Aged (DTLA), a quick, sensitive, standardized screening test designed to assess language disorders in adults and the elderly individuals. In Study 1, we describe the development of the DTLA. In Study 2, we report data on the DTLA's validity and reliability. Finally, in Study 3, we establish normative data for the test. The DTLA has good convergent and discriminant validity as well as good internal consistency and test-retest reliability. Norms for the DTLA obtained from a sample of 545 healthy, community-dwelling, French-speaking adults from 4 French-speaking countries (Belgium, Canada (Quebec), France, and Switzerland) are provided. The development, validation, and standardization of the DTLA constitute a significant effort to meet the need for a language screening test adapted to neurodegenerative diseases.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".