Energy Availability in Athletics: Health, Performance, and Physique
Bibliographic record
Abstract
The reported prevalence of low energy availability (LEA) in female and male track and field athletes is between 18% and 58% with the highest prevalence among athletes in endurance and jump events. In male athletes, LEA may result in reduced testosterone levels and libido along with impaired training capacity. In female track and field athletes, functional hypothalamic amenorrhea as consequence of LEA has been reported among 60% of elite middle- and long-distance athletes and 23% among elite sprinters. Health concerns with functional hypothalamic amenorrhea include impaired bone health, elevated risk for bone stress injury, and cardiovascular disease. Furthermore, LEA negatively affects recovery, muscle mass, neuromuscular function, and increases the risk of injuries and illness that may affect performance negatively. LEA in track and field athletes may occur due to intentional alterations in body mass or body composition, appetite changes, time constraints, or disordered eating behavior. Long-term LEA causes metabolic and physiological adaptations to prevent further weight loss, and athletes may therefore be weight stable yet have impaired physiological function secondary to LEA. Achieving or maintaining a lower body mass or fat levels through long-term LEA may therefore result in impaired health and performance as proposed in the Relative Energy Deficiency in Sport model. Preventive educational programs and screening to identify athletes with LEA are important for early intervention to prevent long-term secondary health consequences. Treatment for athletes is primarily to increase energy availability and often requires a team approach including a sport physician, sports dietitian, physiologist, and psychologist.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".