DTLA-A NEW SCREENING TEST FOR LANGUAGE IMPAIRMENT IN AGING
Bibliographic record
Abstract
Compared to cognitive functions such as working memory and executive functions, language appears to be mostly resistant to age-related decline. However, language is affected in the early stages of major forms of dementia and language deficits are at the core of the clinical portrait of primary progressive aphasias. Primary care providers are frequently faced with patients whose main complaints concern language problems in everyday and professional life. Up to now, no brief, accurate, screening test, which could be applied during routine office visits, was available for language deficits in neurodegenerative diseases. The aim of this study is to fill this important need by developing a handy, sensitive and brief detection test for language impairments in adults and aging. In this presentation, we describe the psychometric properties of the DTLA (Detection Test for Language impairments in Adults and Aging), a new screening test developed in four French-speaking countries (Belgium, Canada, France and Switzerland). We first present the development phase of the DTLA, then we provide normative data for healthy, community-dwelling, French-speaking people from the four countries. Finally, we report data on the convergent and discriminant validity of the DTLA as well as on its test-retest and internal consistency reliability. The use of the DTLA could improve the diagnosis of neurodegenerative diseases, especially those in which language is primarily affected. Ultimately, this will permit patients and their families to receive adequate services at an earlier stage of the disease.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".