Exercise training for neural recovery in a restricted sample of pediatric brain tumor survivors: a controlled clinical trial with crossover of training versus no training
Bibliographic record
Abstract
Background: Exercise promotes repair processes in the mouse brain and improves cognition in both mice and humans. It is not known whether these benefits translate to human brain injury, particularly the significant injury observed in children treated for brain tumors. Methods: We conducted a clinical trial with crossover of exercise training versus no training in a restricted sample of children treated with radiation for brain tumors. The primary outcome was change in brain structure using MRI measures of white matter (ie, fractional anisotropy [FA]) and hippocampal volume [mm3]). The secondary outcome was change in reaction time (RT)/accuracy across tests of attention, processing speed, and short-term memory. Linear mixed modeling was used to test the effects of time, training, training setting, and carryover. Results: Twenty-eight participants completed training in either a group (n=16) or a combined group/home (n=12) setting. Training resulted in increased white matter FA (Δ=0.05, P<.001). A carryover effect was observed for participants ~12 weeks after training (Δ=0.05, P<.001). Training effects were observed for hippocampal volume (Δ=130.98mm3; P=.001) and mean RT (Δ=-457.04ms, P=0.36) but only in the group setting. Related carryover effects for hippocampal volume (Δ=222.81mm3, P=.001), and RT (Δ=-814.90ms, P=.005) were also observed. Decreased RT was predicted by increased FA (R=-0.62, P=.01). There were no changes in accuracy. Conclusions: Exercise training is an effective means for promoting white matter and hippocampal recovery and improving reaction time in children treated with cranial radiation for brain tumors.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".