Antioxidant status, oxidative stress, and damage in elite kayakers after 1 year of training and competition in 2 seasons
Bibliographic record
Abstract
The large volume of training performed by elite athletes throughout the season can translate into a chronic oxidative insult. To study the effects that chronically high training loads have on athletes' redox status, superoxide dismutase (SOD), glutathione reductase, glutathione peroxidase (GPx), and creatine kinase activities; total antioxidant status (TAS); and uric acid, retinol, alpha-tocopherol, alpha-carotene, beta-carotene, lycopene, lutein + zeaxanthin, vitamin C, thiobarbituric acid reactive substances (TBARS), interleukin-6, and cortisol levels were determined in 9 kayakers (6 men) in a competitive period during the first season (June, T1), and in precompetitive (March, T2) and competitive (June, T3) periods during the following season. TAS decreased from the first to the second season (T1 vs. T2, p < 0.001; T1 vs. T3, p < 0.001). TBARS (p = 0.024) decreased from T1 to T2. The alpha-tocopherol increase (p = 0.001) from T1 to T2 lost statistical significance after adjustment for total lipids (p = 0.243). GPx (p = 0.003) increased, while SOD (p < 0.001) and uric acid (p = 0.032) decreased from T2 to T3. Cortisol levels decreased significantly throughout the study (T1 vs. T2, p = 0.042; T2 vs. T3, p = 0.018; T1 vs. T3, p = 0.002). No significant differences were observed for any of the other parameters studied. Antioxidant status changed more within the same season than from one season to another. Redox markers should be monitored throughout the season to detect athletes at an increased oxidative risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".