A Brief Review of Higher Dietary Protein Diets in Weight Loss: A Focus on Athletes
Bibliographic record
Abstract
Thermodynamics dictates that for body weight (i.e. stored substrate) loss to occur a person must ingest less energy than they expend. Athletes, who owing to their oftentimes large daily energy expenditures, may have greater flexibility than non-athletes in this regard; however, they may also have different goals for weight loss. In particular, weight lost may be less important to an athlete than from which compartment the weight is lost: fat or lean. A critical question is thus, what balance of macronutrients might promote a greater fat loss, a relative retention of lean mass, and still allow athletic performance to remain uncompromised? It is the central thesis of this review that dietary protein should be a nutrient around which changes in macronutrient composition should be framed. The requirement for protein to sustain lean mass increases while in negative energy balance and protein, as macronutrient, may have advantages with respect to satiety during energy balance, and it may allow greater fat loss during a negative energy balance. However, athletes should be mindful of the fact that increasing dietary protein intake while in negative energy balance would come at the 'expense' of another macronutrient. Most recently there has been interest in lower carbohydrate diets, which may not allow performance to be sustained given the importance of dietary carbohydrate in high-intensity exercise. The relative merits of higher protein diets for athletes are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".