Antioxidants and Exercise Performance: With a Focus on Vitamin E and C Supplementation
Bibliographic record
Abstract
Antioxidant supplementation, including vitamin E and C supplementation, has recently received recognition among athletes as a possible method for enhancing athletic performance. Increased oxidative stress during exercise results in the production of free radicals, which leads to muscle damage, fatigue, and impaired performance. Despite their negative effects on performance, free radicals may act as signaling molecules enhancing protection against greater physical stress. Current evidence suggests that antioxidant supplementation may impair these adaptations. Apart from athletes training at altitude and those looking for an immediate, short-term performance enhancement, supplementation with vitamin E does not appear to be beneficial. Moreover, the effectiveness of vitamin E and C alone and/or combined on muscle mass and strength have been inconsistent. Given that antioxidant supplements (e.g., vitamin E and C) tend to block anabolic signaling pathways, and thus, impair adaptations to resistance training, special caution should be taken with these supplements. It is recommended that athletes consume a diet rich in fruits and vegetables, which provides vitamins, minerals phytochemicals, and other bioactive compounds to meet the recommended intakes of vitamin E and C.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".