Low energy availability in female athletes: From the lab to the field
Bibliographic record
Abstract
Decades of laboratory research have shown impairments to several body systems after only 4-5 days of strictly controlled consistent low energy availability (LEA); where energy availability (EA) = Energy Intake (EI) - Exercise Energy Expenditure (EEE)/Fat-Free Mass. Meanwhile, cross-sectional reports exist on the interrelatedness of LEA, menstrual dysfunction and impaired bone health in females (the Female Athlete Triad). These findings have demonstrated that LEA is the key underpinning factor behind a broader set of health and performance outcomes, recently termed as Relative Energy Deficiency in Sport (RED-S). There is utmost importance of early screening and diagnosis of RED-S to avoid the development of severe negative health and performance outcomes. However, a significant gap exists between short-term laboratory studies and cross-sectional reports, or clinically field-based situations, of long-term/chronic LEA and no definitive, validated diagnostic tests for RED-S exist. This review aims to highlight methodological challenges related to the assessment of the components of EA equation in the field (e.g. challenges with EI and EEE measures). Due to the uncertainty of these parameters, we propose the use of more chronic "objective" markers of LEA (i.e. blood markers). However, we note that direct extrapolations of laboratory-based outcomes into the field are likely to be problematic due to potentially poor ecological validity and the extreme variability in most athlete's daily EI and EEE. Therefore, we provide a critical appraisal of the scientific literature, highlighting research gaps, and a potential set of leading objective RED-S markers while working in the field. HighlightsDirect application of short-term laboratory-based findings in the field is problematic.Calculation of energy availability (EA) in the field is methodologically challenging and prone to errors.The use of several biomarkers may allow the detection of early exposure to low EA in the female athlete.
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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 | 0.002 | 0.004 |
| 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) | 0.001 | 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".