Association between Aphasia and Acalculia: Analytical Cross-Sectional Study
Bibliographic record
Abstract
Acalculia in aphasic patients should be better investigated in order to understand if it is a simple comorbid or if it is influenced by language disorders. This study aimed to compare the performance on EC301 battery calculation tasks between aphasic and normal subjects and sought to verify a possible association between number processing and calculation skills and linguistic changes in aphasic patients, in order to investigate if language disorders interfere with number processing and calculation. Analytical cross-sectional study with a control group, performed of the Department of Speech and Hearing Disorders of a public university, conducted in the city of São Paulo, Brazil. First, to analyze the specific difficulties encountered in numerical processing and calculation tasks among the aphasic group, aphasic and healthy adult’s performance in specific calculation tasks were compared. The calculation tasks, which had been badly performed by aphasic patients, were selected. Aphasic patients were also submitted to the language tasks from Montreal-Toulouse Protocol: oral and written comprehension, repetition, reading aloud, naming and dictation. We observed that aphasic individuals showed changes in numerical processing and calculation tasks that were not observed in the healthy population. The most important finding of this study was that aphasic individuals showed changes in numerical processing and calculation that were positively associated to their linguistic performance. The strong associations between battery EC301 and linguistic tasks suggest that language disorders interfere with number processing and calculation.
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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 | 0.001 |
| 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) | 0.002 | 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".