The effect of milk on recovery from repeat-sprint cycling in female team-sport athletes
Bibliographic record
Abstract
The consumption of milk following eccentric exercise attenuates the effects of muscle damage in team-sport athletes. However, participation in team sport involves both concentric-eccentric loading and metabolic stress. Therefore, the aim of this study was to investigate the effects of postexercise milk consumption on recovery from a cycling protocol designed to simulate the metabolic demands of team sport. Ten female team-sport athletes participated in a randomised crossover investigation. Upon completion of the protocol participants consumed 500 mL of milk (MILK) or 500 mL of an energy-matched carbohydrate (CHO) drink. Muscle function (peak torque, rate of force development, countermovement jump, 20-m sprint), muscle soreness and tiredness, serum creatine kinase, high-sensitivity C-reactive protein, and measures of oxidative stress (protein carbonyls and reduced glutathione/oxidized glutathione (GSH/GSSG) ratio) were determined at pre-exercise and 24 h, 48 h, and 72 h postexercise. MILK had a possible beneficial effect in attenuating losses in peak torque (180°/s) from baseline to 24 h (3.2% ± 7.8% vs. -6.2% ± 7.5%, MILK vs. CHO) and a possible beneficial effect in minimising soreness (baseline-48 h; baseline-72 h) and tiredness (baseline-24 h; baseline-72 h). There was no change in oxidative stress following the exercise protocol, though a likely benefit of milk was observed for GSH/GSSG ratio at baseline-24 h (0.369 ×/÷ 1.89, 1.103 ×/÷ 3.96, MILK vs. CHO). MILK had an unclear effect on all other variables. Consumption of 500 mL of milk after repeat sprint cycling had little to no benefit in minimising losses in peak torque or minimising increases in soreness and tiredness and had no effect on serum markers of muscle damage and inflammation.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".