Fuelling the female athlete: Carbohydrate and protein recommendations
Bibliographic record
Abstract
Optimal carbohydrate and protein intakes are vital for modulating training adaptation, recovery, and exercise performance. However, the research base underpinning contemporary sport nutrition guidelines has largely been conducted in male populations with a lack of consensus on whether the menstrual phase and associated changes in sex hormones allow broad application of these principles to female athletes. The present review will summarise our current understanding of carbohydrate and protein requirements in female athletes across the menstrual cycle and provide a critical analysis on how they compare to male athletes. On the basis of current evidence, we consider it premature to conclude that female athletes require sex specific guidelines in relation to CHO or protein requirements provided energy needs are met. However, there is a need for further research using sport-specific competition and training related exercise protocols that rigorously control for prior exercise, CHO/energy intake, contraceptive use and phase of menstrual cycle. Our overarching recommendation is to use current recommendations as a basis for adopting an individualised approach that takes into account athlete specific training and competition goals whilst also considering personal symptoms associated with the menstrual cycle.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".