Consensus Statements—Optimizing Performance of the Elite Athlete
Bibliographic record
Abstract
The International Consensus Conference "Optimising Performance of the Elite Athlete," held in November 2024, brought together 29 scientists, some coaches, and athletes to establish evidence-based consensus statements aimed at enhancing elite athletic performance and health. The conference addressed critical themes including training strategies, nutrition, female athlete considerations, injury management, and emerging technologies. Key conclusions emphasize individualized, sport-specific approaches to training and nutrition, integrating concurrent training modalities to improve endurance, resilience, and efficiency. Nutrition strategies highlight the importance of tailored energy and macronutrient periodization, recognition of low energy availability risks, and cautious use of dietary supplements. Special attention was directed to female athletes, advocating for improved monitoring of menstrual cycles and hormonal status, while acknowledging current knowledge gaps in hormonal influences on performance and injury risk. Injury prevention remains a challenge, with tendon overuse and Achilles tendon ruptures significantly impacting athlete careers; rehabilitation should rely on criteria-based progression and multidisciplinary input. Emerging technologies, including wearable sensors and multi-omics analyses, hold promise for personalized training and nutrition but require further validation in elite contexts. Despite robust consensus, the panel identified substantial research gaps, particularly regarding female athletes, longitudinal training effects, and efficacy of novel interventions. This consensus provides a practical, scientifically grounded framework to optimize elite athlete performance and health, while underscoring the need for continued research to address outstanding questions and promote inclusive evidence-based practices.
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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 | 0.292 | 0.370 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".