Bone Turnover Markers and Osteokines in Adolescent Female Athletes of High- and Low-Impact Sports Compared With Nonathletic Controls
Bibliographic record
Abstract
This study examined differences in resting concentrations of markers of bone formation and resorption, and osteokines between female adolescent (12-16 y) swimmers, soccer players, and nonathletic controls. Resting, morning blood samples were obtained after an overnight fast from 20 swimmers, 20 soccer players, and 20 nonathletic controls, matched for age. carboxyl-terminal cross-linking telopeptide of type I collagen (CTX), amino-terminal propeptide of type I collagen (P1NP), total osteocalcin (OC), sclerostin, osteoprotegerin (OPG), and receptor activator of nuclear factor kappa B ligand (RANKL) were analyzed in serum. After controlling for percent body fat, there were no significant differences between swimmers and nonathletic controls in any of the measured markers. In contrast, soccer players had significantly higher P1NP (89.5 [25.6] ng·mL-1), OC (57.6 [22.9] ng·mL-1), and OPG (1052.5 [612.6] pg·mL-1) compared with both swimmers (P1NP: 66.5 [20.9] ng·mL-1; OC: 24.9 [12.5] ng·mL-1; OPG: 275.2 [83.8] pg·mL-1) and controls (P1NP: 58.5 [16.2] ng·mL-1; OC: 23.2 [11.9] ng·mL-1; OPG: 265.4 [97.6] pg·mL-1), with no differences in CTX, sclerostin, and RANKL. These results suggest that bone formation is higher in adolescent females engaged in high-impact sports like soccer compared with swimmers and controls.
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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 | 0.001 | 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".