Summary of the 2024 Professionals in Nutrition for Exercise and Sport “10 Questions/10 Experts” Session—Hot Topics for the Paris Olympic Games
Bibliographic record
Abstract
This article provides a recap of the 10 Questions/10 Experts session at the 2024 American College of Sports Medicine Annual Meeting. Each of the speakers considered the validity of common "myths," while providing evidence-based opinions to support, or, bust, myths addressing the following questions: (1) Would 100 g/hr of carbohydrate be advisable for the Olympic Cycling Road race? (2) Is there an advantage in the marathon of ingesting bicarbonate as a hydrogel product? (3) Can genotyping be used to individualize caffeine supplementation in football? (4) Should low fluid consumers drink more to improve 1,500-m track performance? (5) Do urinary markers of dehydration predict poor basketball performance? (6) Do placebo effects influence 10-km track performance? (7) Should combat athletes make weight using glucagon-like peptide-1 receptor agonists? (8) Would crushed ice ingestion help tennis umpires make better decisions in the heat? (9) Are collagen supplements useful to reduce tendon and ligament injuries in volleyball? and (10) Should female athletes plan their training and diet according to their menstrual cycle? This article describes the content of each of the presentations including the most important outcomes and conclusions drawn by the presenters.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.206 | 0.092 |
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".