Effects of postexercise milk consumption on whole body protein balance in youth
Bibliographic record
Abstract
In adults, adding protein to a postexercise beverage increases muscle protein turnover and replenishes amino acid stores. Recent focus has shifted toward the use of bovine-based milk and milk products as potential postexercise beverages; however, little is known about how this research translates to the pediatric population. Twenty-eight (15 girls) pre- to early pubertal (PEP, 7-11 yr) and mid- to late-pubertal (MLP, 14-17 yr) children consumed an oral dose of [(15)N]glycine prior to performing 2 × 20-min cycling bouts at 60% V̇O(2 peak) in a warm environment (34.5°C, 47.3% relative humidity). Following exercise, participants consumed either water (W), a carbohydrate-electrolyte solution (CES), or skim milk (SM) in a randomized, cross-over fashion in a volume equal to 100% of their body mass loss during exercise. Whole body nitrogen turnover (Q), protein synthesis (S), protein breakdown (B), and whole body protein balance (WBPB) were measured over 16 h. Protein intake from SM was 0.40 ± 0.10 g/kg. Over 16 h, Q and S were significantly greater (P < 0.01) with SM than W and CES. B demonstrated a trend for a main effect for beverage (P = 0.063). WBPB was more negative (P < 0.01) with W and CES than with SM. In the SM trial, WBPB was positive in PEP, although it remained negative in MLP. Boys exhibited significantly more negative WBPB than girls (P < 0.05). Postexercise milk consumption enhances WBPB compared with W and CES; however, additional protein intake may be required to sustain a net anabolic environment over 16 h.
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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) | 0.000 | 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 | 0.000 | 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".