Low Energy Availability Is Difficult to Assess but Outcomes Have Large Impact on Bone Injury Rates in Elite Distance Athletes
Why this work is in the frame
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Bibliographic record
Abstract
We aimed to (a) report energy availability (EA), metabolic/reproductive function, bone mineral density, and injury/illness rates in national/world-class female and male distance athletes and (b) investigate the robustness of various diagnostic criteria from the Female Athlete Triad (Triad), Low Energy Availability in Females Questionnaire, and relative energy deficiency in sport (RED-S) tools to identify risks associated with low EA. Athletes were distinguished according to benchmarks of reproductive function (amenorrheic [ n = 13] vs. eumenorrheic [ n = 22], low [lowest quartile of reference range; n = 10] versus normal testosterone [ n = 14]), and EA calculated from 7-day food and training diaries (< or >30 kcal·kg −1 fat-free mass·day −1 ). Sex hormones ( p < .001), triiodothyronine ( p < .05), and bone mineral density (females, p < .05) were significantly lower in amenorrheic (37%) and low testosterone (40%; 15.1 ± 3.0 nmol/L) athletes, and bone injuries were ∼4.5-fold more prevalent in amenorrheic (effect size = 0.85, large) and low testosterone (effect size = 0.52, moderate) groups compared with others. Categorization of females and males using Triad or RED-S tools revealed that higher risk groups had significantly lower triiodothyronine (female and male Triad and RED-S: p < .05) and higher number of all-time fractures (male Triad: p < .001; male RED-S and female Triad: p < .01) as well as nonsignificant but markedly (up to 10-fold) higher number of training days lost to bone injuries during the preceding year. Based on the cross-sectional analysis, current reproductive function (questionnaires/blood hormone concentrations) appears to provide a more objective and accurate marker of optimal energy for health than the more error-prone and time-consuming dietary and training estimation of EA. This study also offers novel findings that athlete health is associated with EA indices.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it