Iron monitoring of male and female rugby sevens players over an international season
Bibliographic record
Abstract
BACKGROUND: Given the likely influence that high training loads, contact-induced hemolysis and female-specific requirements have on the incidence of iron deficiency, characterizing the direction and magnitude of fluctuations in iron status over an international season is important for managing player health and physical performance in rugby sevens. METHODS: Australian national male (N.=27) and female (N.=23) rugby sevens players undertook blood tests at pre-season, mid-season, and end-season. Hemoglobin (Hb), hematocrit (Hct), ferritin, transferrin and transferrin saturation were quantified. Female athletes also reported oral contraceptive use and a subset (N.=7) provided 7-day food diaries to quantify iron intake. RESULTS: Male players typically had a three-fold higher ferritin concentration than females. Pre-season ferritin concentrations in male (151±66 µg/L) and female (51±24 µg/L) players declined substantially (~20%) by mid-season but recovered by end-season. Over the season 23% of female players were classified as iron deficient (ferritin <30 µg/L) and prescribed supplementation. The greatest incidence of iron deficiency in female players occurred mid-season (30%). Oral contraception and dietary iron intake had an unclear influence on female players' ferritin concentration, while age was largely positively correlated (r=0.66±~0.33). CONCLUSIONS: Given the relatively low ferritin concentrations evident in female rugby sevens players, and the potential for a further decline midway through a season when physical load may be at its highest, 6-monthly hematological reviews are suggested in combination with dietary management. Annual screening may be beneficial for male players, with further monitoring only when clinically indicated.
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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 | 0.001 |
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".