Considerations for protein intake in managing weight loss in athletes
Why this work is in the frame
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Bibliographic record
Abstract
A large body of evidence now shows that higher protein intakes (2-3 times the protein Recommended Dietary Allowance (RDA) of 0.8 g/kg/d) during periods of energy restriction can enhance fat-free mass (FFM) preservation, particularly when combined with exercise. The mechanisms underpinning the FFM-sparing effect of higher protein diets remain to be fully elucidated but may relate to the maintenance of the anabolic sensitivity of skeletal muscle to protein ingestion. From a practical point of view, athletes aiming to reduce fat mass and preserve FFM should be advised to consume protein intakes in the range of ∼1.8-2.7 g kg(-1) d(-1) (or ∼2.3-3.1 g kg(-1) FFM) in combination with a moderate energy deficit (-500 kcal) and the performance of some form of resistance exercise. The target level of protein intake within this recommended range requires consideration of a number of case-specific factors including the athlete's body composition, habitual protein intake and broader nutrition goals. Athletes should focus on consuming high-quality protein sources, aiming to consume protein feedings evenly spaced throughout the day. Post-exercise consumption of 0.25-0.3 g protein meal(-1) from protein sources with high leucine content and rapid digestion kinetics (i.e. whey protein) is recommended to optimise exercise-induced muscle protein synthesis. When protein is consumed as part of a mixed macronutrient meal and/or before bed slightly higher protein doses may be optimal.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it