Nutritional needs of elite endurance athletes. Part II: Dietary protein and the potential role of caffeine and creatine
Bibliographic record
Abstract
Abstract Amino acids contribute between 2–8% of the energy needs during endurance exercise. Endurance exercise training leads to an adaptive reduction in the oxidation of amino acids at the same absolute exercise intensity, however, the capacity to oxidize amino acids goes up due to the increase in the total amount of the rate limiting enzyme, branched chain 2‐oxo‐acid dehydrogenase. There appears to be a modest increase (range = 12–95%) in protein requirements only for very well trained athletes who are actively training. Although the majority of athletes will have ample dietary protein to meet any increased need, those on a hypoenergetic diet or during extreme periods of physical stress may need dietary manipulation to accommodate the need. Caffeine is a trimethylxanthine derivative that is common in many foods and beverages. The consumption of caffeine (3–7 mg/kg) prior to endurance exercise improves performance for habitual and non‐habitual consumers. The ergogenic effect is likely due to a direct effect on muscle contractility and not via an enhancement of fatty acid oxidation. Creatine is important in intra‐cellular energy shuttling and in cellular fluid regulation. Creatine monohydrate supplementation (20 g/d X 3–5 days) increases fat‐free mass, improves muscle strength during repetitive high intensity contractions and increases fat‐free mass accumulation and strength during a period of weight training. Given the increase in weight, there are likely neutral or even performance reducing effects in sports that are influenced by body mass (i.e., running, hill climbing cycling).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".