Food for thought: Physiological considerations for nutritional ergogenic efficacy
Bibliographic record
Abstract
Top-class athletes have optimized their athletic performance largely through adequate training, nutrition, recovery, and sleep. A key component of sports nutrition is the utilization of nutritional ergogenic aids, which may provide a small but significant increase in athletic performance. Over the last decade, there has been an exponential increase in the consumption of nutritional ergogenic aids, where over 80% of young athletes report using at least one nutritional ergogenic aid for training and/or competition. Accordingly, due to their extensive use, there is a growing need for strong scientific investigations validating or invalidating the efficacy of novel nutritional ergogenic aids. Notably, an overview of the physiological considerations that play key roles in determining ergogenic efficacy is currently lacking. Therefore, in this brief review, we discuss important physiological considerations that contribute to ergogenic efficacy for nutritional ergogenic aids that are orally ingested including (1) the impact of first pass metabolism, (2) rises in systemic concentrations, and (3) interactions with the target tissue. In addition, we explore mouth rinsing as an alternate route of ergogenic efficacy that bypasses the physiological hurdles of first pass metabolism via direct stimulation of the central nervous system. Moreover, we provide real-world examples and discuss several practical factors that can alter the efficacy of nutritional ergogenic aids including human variability, dosing protocols, training status, sex differences, and the placebo effect. Taking these physiological considerations into account will strengthen the quality and impact of the literature regarding the efficacy of potential ergogenic aids for top-class athletes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".