Menstrual blood losses and body mass index are associated with serum ferritin concentrations among female varsity athletes
Bibliographic record
Abstract
Preventing and treating iron deficiency are important components in the nutritional care of female varsity athletes, as these interventions may improve aerobic endurance and athletic performance. We examined the factors associated with ferritin concentration in 30 female varsity athletes (18–30 years) at the University of British Columbia in Vancouver, Canada. Biochemical indicators of iron and inflammation status, dietary intake, supplementation practices, weight, height, and menstrual blood losses were assessed. Iron deficiency prevalence was 20% (n = 6/30; inflammation-adjusted ferritin <15 μg/L). Multiple linear regression was used to assess the associations of a number of independent explanatory variables with log-transformed serum ferritin (μg/L) as the continuous outcome variable. A 1-unit increase in body mass index (BMI; kg/m2) was associated with 22% (95% CI: 9%–37%) higher mean ferritin concentrations, and a 1-point increase in menstrual loss score was associated with 1% (95% CI: 1%–2%) lower ferritin concentrations. Hemoglobin and hepcidin concentrations, inflammation biomarkers, consumption of iron supplements in any form or dose for ≥3 days/week, and age were not significantly associated with ferritin concentrations in the final adjusted model. Novelty Estimated monthly menstrual losses and BMI were associated with serum ferritin concentrations in female athletes in our study. These are easy-to-measure, noninvasive measurements that should be considered in the assessment of risk of iron deficiency in female 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 | 0.002 |
| 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) | 0.001 | 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".