Direct and indirect impact of low energy availability on sports performance
Bibliographic record
Abstract
Low energy availability (LEA) occurs inadvertently and purposefully in many athletes across numerous sports; and well planned, supervised periods with moderate LEA can improve body composition and power to weight ratio possibly enhancing performance in some sports. LEA however has the potential to have negative effects on a multitude of physiological and psychological systems in female and male athletes. Systems such as the endocrine, cardiovascular, metabolism, reproductive, immune, mental perception, and motivation as well as behaviors can all be impacted by severe (serious and/or prolonged or chronic) LEA. Such widely diverse effects can influence the health status, training adaptation, and performance outcomes of athletes leading to both direct changes (e.g., decreased strength and endurance) as well as indirect changes (e.g., reduced training response, increased risk of injury) in performance. To date, performance implications have not been well examined relative to LEA. Therefore, the intent of this narrative review is to characterize the effects of short-, medium-, and long-term exposure to LEA on direct and indirect sports performance outcomes. In doing so we have focused both on laboratory settings as well as descriptive athletic case-study-type experiential evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".