Managing Female Athlete Health: Auditing the Representation of Female versus Male Participants among Research in Supplements to Manage Diagnosed Micronutrient Issues
Bibliographic record
Abstract
Micronutrient deficiencies and sub-optimal intakes among female athletes are a concern and are commonly prevented or treated with medical supplements. However, it is unclear how well women have been considered in the research underpinning current supplementation practices. We conducted an audit of the literature supporting the use of calcium, iron, and vitamin D. Of the 299 studies, including 25,171 participants, the majority (71%) of participants were women. Studies with exclusively female cohorts (37%) were also more prevalent than those examining males in isolation (31%). However, study designs considering divergent responses between sexes were sparse, accounting for 7% of the literature. Moreover, despite the abundance of female participants, the quality and quantity of the literature specific to female athletes was poor. Just 32% of studies including women defined menstrual status, while none implemented best-practice methodologies regarding ovarian hormonal control. Additionally, only 10% of studies included highly trained female athletes. Investigations of calcium supplementation were particularly lacking, with just two studies conducted in highly trained women. New research should focus on high-quality investigations specific to female athletes, alongside evaluating sex-based differences in the response to calcium, iron, and vitamin D, thus ensuring the specific needs of women have been considered in current protocols involving medical supplements.
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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 | 0.127 | 0.187 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".