Influence of schooling on language abilities of adults without linguistic disorders
Bibliographic record
Abstract
CONTEXT AND OBJECTIVE: In order to properly assess language, sociodemographic variables that can influence the linguistic performance of individuals with or without linguistic disorders need to be taken into account. The aim of this study was to evaluate the influence of schooling and age on the results from the Montreal Toulouse (Modified MT Beta-86) language assessment test among individuals without linguistic disorders. DESIGN AND SETTING: Cross-sectional study carried out between March 2006 and August 2007 in the Speech, Language and Hearing Pathology Department of Universidade Federal de São Paulo (Unifesp), São Paulo, Brazil. METHODS: Eighty volunteers were selected. Schooling was stratified into three bands: A (1-4 years), B (5-8 years) and C (nine years and over). The age range was from 17 to 80 years. All the subjects underwent the Montreal Toulouse (Modified MT Beta-86) language assessment protocol. RESULTS: Statistically significant differences were found in relation to schooling levels, in the tasks of oral comprehension, reading, graphical comprehension, naming, lexical availability, dictation, graphical naming of actions and number reading. Statistically significant age-related differences in dictation and lexical availability tasks were observed. CONCLUSIONS: The Montreal Toulouse (Modified MT Beta-86) test seems to be sensitive to variations in schooling and age. These variables should be taken into account when this test is used for assessing patients with brain damage.
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".