QOS-06REPAIRING THE BRAIN WITH PHYSICAL EXERCISE: AN EXERCISE TRIAL IN PEDIATRIC BRAIN TUMOR SURVIVORS. INSIGHTS FROM CORTICAL THICKNESS ANALYSIS AND DEFORMATION BASED MORPHOMETRY
Bibliographic record
Abstract
Due to the tumour itself, surgery, but also due to side effects of cranial radiation and chemotherapy, children cured of their brain tumours are often left with significant brain injury and cognitive deficits. Given the rapidly growing evidence of benefits of physical activity for the brain we conducted a 12-week program to examine whether aerobic exercise can stimulate brain repair processes in pediatric brain tumor survivors. Children participated in 90-minutes of group based aerobic activities 3 times per week in a “group only” (n = 16) or “combined group/home” (n = 12) setting. Standard 3D anatomical MRI scans were collected to examine changes in brain anatomy. Previous analysis have shown that this exercise intervention results in improvement in white matter metrics in all children as well as increases in hippocampal volume and decreased reaction time in “group only” setting. In this study we extended our prior analysis to cortical thickness analysis and deformation based morphometry (DBM). Based on regional analysis of changes in cortical thickness a significant effect of training on thickness of right premotor cortex was observed in the “group only” setting. Additionally, increases in cortical thickness within: right and left temporal pole, right parietal lobe and right and left parahippocampal gyrus were also observed. DBM analysis revealed increases in volume of white matter underlying: right motor cortex, right somatosensory cortex and right parietal lobe regardless of the training setting. This data suggest that aerobic exercise may be an effective intervention in fostering neurobehavioural recovery in children treated for brain tumours.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".