Iron supplementation prevents a decline in iron stores and enhances strength performance in elite female volleyball players during the competitive season
Bibliographic record
Abstract
The primary aim of this study was to examine the effects of 11 weeks of iron supplementation on hematological and strength markers in elite female volleyball players. Twenty-two volleyball players (aged 27.0 ± 5.6 years) from 2 Spanish First National League teams participated and were counterbalanced into 1 of 2 groups based upon iron status: (i) control group (CG, n = 11); or (ii) iron treatment group (ITG, n = 11), which received 325 mg/day of ferrous sulphate daily. Subjects performed their team's regimen of training or match play every day. Both groups were tested for hematological and strength levels at 2 points: (i) baseline (T0, before preseason) and (ii) 11 weeks later (T11, post-testing). Hematological parameters were serum iron (sFe), serum ferritin (FER), transferrin saturation index (TSI), and hemoglobin (Hb); strength assessments were bench press, military press, half-squat, power clean, clean and jerk, and pull-over. CG experienced a significant decrease (p < 0.05) for sFe (T0, 112.7 ± 31.5; T11, 69.0 ± 20.5 μg·dL(-1); -33.9%), FER (T0, 60.2 ± 28.6; T11, 38.2 ± 16.4 ng·mL(-1); -34.6%), TSI (T0, 29.4% ± 9.5%; T11, 17.4% ± 5.1%; -35.3%), and Hb (T0, 14.1 ± 1.0; T11, 13.0 ± 0.8 g·L(-1); -7.44%); however, ITG experienced no changes (p > 0.05). Consequently, in ITG all hematological parameters were significantly greater (p < 0.05) than CG at T11. There was greater (p < 0.05) percent increase in the clean and jerk (CG: +5.1% ± 20.9 vs. ITG: +29.0% ± 21.3%), power clean (CG: -5.8% ± 30.3% vs. ITG: +44.6% ± 56.6%), and total mean strength (CG: +10.9% ± 3.2% vs. ITG: +26.2% ± 3.6%) in ITG. Our findings suggest that oral iron supplementation prevents iron loss and enhances strength in female volleyball players during the competitive season.
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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".