Optimizing an exercise training program in pediatric brain tumour survivors: Does timing postradiotherapy matter?
Bibliographic record
Abstract
Abstract Background While exercise training (ET) programs show positive outcomes in cognition, motor function, and physical fitness in pediatric brain tumor (PBT) survivors, little is known about the optimal timing of intervention. The aim of this work was to explore the feasibility and benefits of ET based on its timing after radiotherapy. Methods This retrospective analysis (ClinicalTrials.gov, NCT01944761) analyzed data based on the timing of PBT survivors' participation in an ET program relative to their completion of radiotherapy: <2 years (n = 9), 2–5 years (n = 10), and > 5 years (n = 13). We used repeated measures analysis of variance to compare feasibility and efficacy indicators among groups, as well as correlation analysis between ET program timing postradiotherapy and preliminary treatment effects on cognition, motor function and physical fitness outcomes. Results Two to five years postradiotherapy was the optimal time period in terms of adherence (88.5%), retention (100%), and satisfaction (more fun, more enjoyable and recommend it more to other children). However, the benefits of ET program on memory recognition (r = −0.379, P = .047) and accuracy (r = −0.430, P = .032) decreased with increased time postradiotherapy. Motor function improved in all groups, with greater improvements in bilateral coordination (P = .043) earlier postradiotherapy, and in running (P = .043) later postradiotherapy. The greatest improvement in pro-rated work rate occurred in the < 2-year group (P = .008). Conclusion Participation in an ET program should be offered as part of routine postradiotherapy care in the first 1–2 years and strongly encouraged in the first 5 years.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".