QOL-54. THE RELATIONSHIP BETWEEN MATHEMATICS AND WORKING MEMORY IN PAEDIATRIC BRAIN TUMOUR SURVIVORS TREATED WITH RADIATION
Bibliographic record
Abstract
Brain tumours are the leading cause of death and disability among childhood diseases in developed countries. Cranial-spinal irradiation (CSI) is frequently required for effective tumour control but is associated with significant cognitive morbidity. Research has documented significant learning and cognitive difficulties, particularly in mathematics and working memory following CSI. Previous research has shown that in healthy controls, math skills and working memory are strongly linked. The relationship between working memory and math competences in pediatric brain tumour survivors (PBTS) has yet to be established. The goal of the study was to investigate the relationship between working memory and mathematic abilities in PBTS treated with CSI. We administered standardized measures of working memory, intelligence and math skills to 27 PBTS. Tumour and treatment related effects were combined into a single composite score and was included as a predictor variable. Multiple linear regression analyses were used to investigate the unique contribution of working memory on mathematics. The results revealed that both intelligence and verbal working memory were significant predictors of various mathematical competencies. However, verbal working memory emerged as the most significant predictor for both arithmetic and math fluency skills, uniquely accounting for 38% and 37% of the variance, respectively. While intellectual functioning had the greatest influence on math problem solving skills, verbal working memory uniquely accounted for 15% of the variance. Overall, mathematical difficulties in PBTS are, in part, related to deficits in verbal working memory. The findings suggest that interventions targeting working memory may improve math skills within this population.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".