Female Athlete Representation and Dietary Control Methods Among Studies Assessing Chronic Carbohydrate Approaches to Support Training
Bibliographic record
Abstract
The aim of this audit was to assess the representation of female athletes, dietary control methods, and gold standard female methodology that underpins the current guidelines for chronic carbohydrate (CHO) intake strategies for athlete daily training diets. Using a standardized audit, 281 studies were identified that examined high versus moderate CHO, periodized CHO availability, and/or low CHO, high fat diets. There were 3,735 total participants across these studies with only ∼16% of participants being women. Few studies utilized a design that specifically considered females, with only 16 studies (∼6%) including a female-only cohort and six studies (∼2%) with a sex-based comparison in their statistical procedure, in comparison to the 217 studies (∼77%) including a male-only cohort. Most studies (∼72%) did not provide sufficient information to define the menstrual status of participants, and of the 18 studies that did, optimal methodology for control of ovarian hormones was only noted in one study. While ∼40% of male-only studies provided all food and beverages to participants, only ∼20% of studies with a female-specific design used this approach for dietary control. Most studies did not implement strategies to ensure compliance to dietary interventions and/or control energy intake during dietary interventions. The literature that has contributed to the current guidelines for daily CHO intake is lacking in research that is specific to, or adequately addresses, the female athlete. Redressing this imbalance is of high priority to ensure that the female athlete receives evidence-based recommendations that consider her specific needs.
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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 | 0.281 | 0.457 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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; 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".