Influence of sport type and gender on bone turnover markers in young athletes
Bibliographic record
Abstract
Abstract Background Exercise is beneficial to bone health. However, little is known about the interaction effect of gender and sport type on bone turnover in young athletes. This study aimed to examine the influence of gender and sports categories (high, medium, and low impact) on bone turnover: reabsorption markers–osteocalcin, calcium, inorganic phosphate (IP), alkaline phosphatase (ALP), and resorption marker–cross-linked N-telopeptides of type 1 collagen (NTx) among a university’s undergraduate athletes. Methods The study was an ex-post facto design involving forty-seven purposively recruited gender- and sport-type-matched undergraduate athletes whose demographic characteristics and BMI were obtained. Participants’ 5 mL antecubital blood samples were collected and analysed for serum levels of osteocalcin, calcium, IP, ALP, and NTx using standard laboratory protocols, Bio-Tek spectrometer, and KC4 (3.3) software. Data were analysed using descriptive statistics and two-way ANOVA. Results The study involved 24 females and 23 males (n = 47) aged 22.15 ± 3.35 years with an average BMI of 23.34 ± 4.66. There was no significant gender effect on the biomarkers. However, there was a significant effect of the sports category on IP (F = 4.307, p = 0.020), calcium (F = 6.807, p = 0.003), and ALP serum levels (F = 11.511, p < 0.001). Specifically, mid-impact sports participants had a higher IP than the low-impact group (mean difference [MD] = 0.81 mg/dL, p = 0.036). Low-impact had a higher calcium level than mid-impact (MD = 0.40 mg/dL, p = 0.022) and high-impact (MD = 0.49 mg/dL, p = 0.003). Conversely, low-impact had lower ALP than mid-impact (MD = − 11.13 U/L, p = 0.013) and high-impact (MD = − 17.44 IU/L, p < 0.001). Conclusion Moderate to high-impact sports positively affected bone turnover in young athletes. However, gender had no significant impact.
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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) | 0.002 | 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".