The Potential of Physical Exercise to Mitigate Radiation Damage—A Systematic Review
Bibliographic record
Abstract
There is a need to investigate new countermeasures against the detrimental effects of ionizing radiation as deep space exploration missions are on the horizon. Objective: In this systematic review, the effects of physical exercise upon ionizing radiation-induced damage were evaluated. Methods: Systematic searches were performed in Medline, Embase, Cochrane library, and the databases from space agencies. Of 2,798 publications that were screened, 22 studies contained relevant data that were further extracted and analyzed. Risk of bias of included studies was assessed. Due to the high level of heterogeneity, meta-analysis was not performed. Five outcome groups were assessed by calculating Hedges' g effect sizes and visualized using effect size plots. Results: Exercise decreased radiation-induced DNA damage, oxidative stress, and inflammation, while increasing antioxidant activity. Although the results were highly heterogeneous, there was evidence for a beneficial effect of exercise in cellular, clinical, and functional outcomes. Conclusions: Out of 72 outcomes, 68 showed a beneficial effect of physical training when exposed to ionizing radiation. As the first study to investigate a potential protective mechanism of physical exercise against radiation effects in a systematic review, the current findings may help inform medical capabilities of human spaceflight and may also be relevant for terrestrial clinical care such as radiation oncology.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".