The Systemic Effects of Exercise on Regulators of Muscle and Bone in Girls and Women
Bibliographic record
Abstract
PURPOSE: To assess the systemic effects of an acute bout of moderate-intensity exercise on factors that are known to regulate muscle and bone growth in prepubertal girls and women. METHODS: A total of 12 prepubertal girls (8-10 y) and 12 women (20-30 y) cycled at 60% maximal oxygen uptake for 1 hour followed by 1 hour recovery. Blood samples were collected at rest, mid-exercise, end of exercise, mid-recovery, and end of recovery. Plasma was analyzed for interleukin-6, chemokine ligand 1, fibroblast growth factor-2, total insulin growth factor-1 (IGF-1), and free IGF-1 using enzyme-linked immunosorbent assays assays. RESULTS: Both groups had similar concentrations of systemic factors at baseline with the exception of free IGF-1, which was higher in girls (P = .001). Interleukin-6 response was lower in girls versus women (P = .04), with a difference of +105.1% at end of exercise (P < .001), +113.5% at mid-recovery (P = .001), and +93.2% at end of recovery (P = .02). Girls and women exhibited significant declines in chemokine ligand 1, fibroblast growth factor-2, and total IGF-1 during recovery. CONCLUSION: Compared with women, an acute bout of moderate-intensity exercise in girls elicits a lower inflammatory response, suggesting that other mechanisms may be more important for driving the anabolic effects of exercise on muscle and bone in girls.
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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 | 0.000 |
| 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) | 0.002 | 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".