Bone and Inflammatory Responses to Training in Female Rowers over an Olympic Year
Bibliographic record
Abstract
INTRODUCTION/PURPOSE: To examine whether fluctuations in training load during an Olympic year lead to changes in bone mineral densities and factors that regulate bone (sclerostin, osteoprotegerin and receptor activator of nuclear factor kappa-B ligand), energy metabolism (insulin-like growth factor-1 and leptin), and inflammation (tumor necrosis factor-α and interleukin 6) in elite heavyweight female rowers. METHODS: Blood samples were drawn from 15 female heavyweight rowers (27.0 ± 0.8 yr, 80.9 ± 1.3 kg, 179.4 ± 1.4 cm) at baseline (T1-45 wk before Olympic Games) and after 7, 9, 20, 25, and 42 wk (T1-6, respectively). Ongoing nutritional counseling was provided. Total weekly training load was recorded over the week before each time point. Bone mineral density (BMD) was measured by dual energy x-ray absorptiometry at T1 and T6. RESULTS: Total BMD increased significantly before to after training (+0.02 g·cm), but was below the least significant change (±0.04 g·cm). Osteoprotegerin, insulin-like growth factor-1, and leptin remained stable across all time points. Fluctuations in training load (high vs low) were accompanied by parallel changes in tumor necrosis factor-α (2.1 ± 0.2 vs 1.5 ± 0.2 pg·mL), interleukin 6 (1.2 ± 0.08 vs 0.8 ± 0.09 pg·mL), and sclerostin (high: 993 ± 109 vs low: 741 ± 104 pg·mL). CONCLUSIONS: In this population of young female athletes with suitable energy availability, sclerostin and inflammation markers responded to fluctuations in training load, whereas BMD and bone mineral content were stable during the season, suggesting that training load periodization is not harmful for the bone health in athletes.
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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) | 0.001 | 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".