Signs of low energy availability in elite male volleyball athletes but no association with risk of bone stress injury and patellar tendinopathy
Bibliographic record
Abstract
ABSTRACT Relative Energy Deficiency in Sport (RED‐S) syndrome is associated with undesirable health and performance outcomes. The aetiology of RED‐S syndrome is low energy availability (LEA). LEA has been reported in male athletes in various sports, but there is little information in team sports. Therefore, the aims of this study were to assess the point‐prevalence of surrogate markers of LEA in elite male volleyball players and examine the association between low and normal total‐testosterone (TES) on endocrine markers, resting metabolic rate, bone mineral density (BMD), and history of injury/illness. Using a cross‐sectional design, 22 elite male volleyball players underwent anthropometric, dual‐energy X‐ray absorptiometry (DEXA or DXA) and resting metabolic rate testing, bloodwork, dietary analysis, the three‐factor eating questionnaire–R18, injury/illness questionnaire and Victorian Institute of Sport Assessment – patellar tendon questionnaire. The primary finding of this investigation was that 36% of athletes had ≥2 surrogate markers of LEA. Although fasted insulin was lower and cortisol was higher in players with low‐total TES, low BMD, low RMR and various other endocrine markers linked to LEA were not observed. More research is required to define surrogate markers of LEA in male 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".