Fueling the Female Athlete: Auditing Her Representation in Studies of Acute Carbohydrate Intake for Exercise
Bibliographic record
Abstract
PURPOSE: The aim of this audit was to assess the representation of female athletes within the literature that has led to current guidelines for carbohydrate (CHO) intake in the acute periods surrounding exercise and the quality of this research. METHODS: We conducted a standardized audit of research assessing CHO loading protocols, CHO mouth rinse, and CHO intake before, during, and after exercise. RESULTS: A total of 937 studies were identified in this audit. There were a total of 11,202 participants across these studies, with only ~11% being women. Most studies involved male-only cohorts (~79%), with a mere 38 studies (~4%) involving female-only cohorts and 14 studies (~2%) including a methodological design for comparison of sex-based responses. The frequent use of incorrect terminology surrounding menstrual status and the failure of most studies (~69%) to provide sufficient information on the menstrual status of participants suggests incomplete understanding and concern for female-specific considerations among researchers. Of the 197 studies that included women, only 13 (~7%) provided evidence of acceptable methodological control of ovarian hormones, and no study met all best-practice recommendations. Of these 13 studies, only half also provided sufficient information regarding the athletic caliber of participants. The topics that received such scrutiny were CHO loading protocols and CHO intake during exercise. CONCLUSIONS: The literature that underpins the current guidelines for CHO intake in the acute periods around exercise is lacking in high-quality research that can contribute knowledge specific to the female athlete and sex-based differences. New research that considers ovarian hormones and sex-based differences is needed to ensure that the recommendations for acute CHO fueling provided to female athletes are evidence based.
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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 | 0.394 | 0.580 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".