Performance of older adults on MTL- BR Battery: ARE There differences in healthy aging? A pilot study
Bibliographic record
Abstract
OBJECTIVES: Characterize and better understand differences that can occur in performing language tasks with advancing age in order to observe if the current norms of MTL-BR Battery obtained with individuals up 75 years of age could also be applied to old-old-individuals. METHODS: 80 participants, stratified into two age groups: 40 individuals aged 60-75 and 40 individuals aged 76-90 years, were submitted to an extensive language assessment by Montreal Toulouse Language Battery-Brazilian version (MTL-BR). RESULTS: A statistically significant difference was found between both groups on the tasks of oral comprehension of sentences, narrative discourse (oral and written), written dictation, repetition of words, reading aloud of sentences and numbers, verbal naming and written numerical calculation. DISCUSSION: The study showed that aging negatively affected performance on some language tasks. The results obtained in all tasks may be useful for comparative analyses in the clinical evaluation of old-old individuals with language complaints submitted to the MTL-BR Battery. On the one hand, subtests that typically exhibit ceiling effects in healthy populations, if found to be impaired, may clearly indicate the presence of a language disorder. On the other hand, data related to tasks that showed age-related differences should be interpreted with caution. In this sense, the data obtained on the performance of old-old individuals may guide the interpretation of the language assessment using the MTL-BR Battery or similar language assessment procedures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".