Understanding neurocognitive outcomes in Pediatric Brain Tumour Survivors in context: Examining medical and sociodemographic risk factors
Bibliographic record
Abstract
Background . Pediatric Brain Tumour Survivors (PBTS) are at risk of neurocognitive impairments. This study assesses both objective and parent-reported cognitive functioning in PBTS and examines how various factors (medical and socio-demographic) may contribute to cognitive outcomes. PBTS ( n = 100) were on average 5.77 years old at diagnosis, 12.36 years from diagnosis, and 47% female. Method . Participant IQ was measured using the full-scale IQ of the WISC-IV and WISC-V, and executive function using the BRIEF2 Global Executive Composite. Examined contributors included: age, sex, tumour location, time since diagnosis, radiation type, chemotherapy dose (high versus low), parent’s education level and mother’s partnered status. Results . Higher IQ was correlated with higher executive function skills. Differential patterns were observed with socio-demographic variables influencing working memory, while radiation influenced processing speed. Higher education level in both mothers and fathers and maternal partnered status were associated with higher child working memory. Proton radiation was associated with higher processing speed scores. However, only time since diagnosis contributed to total IQ and working memory in multiple linear regression analyses. Conclusion . The findings shed light on the sparsely examined domain of the impact of socio-demographic variables on neurocognitive outcomes in PBTS. Time since diagnosis remains a significant predictor of cognitive performance, accentuating the need for early identification and intervention in PBTS.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".