Mapping the complexities of Relative Energy Deficiency in Sport (REDs): development of a physiological model by a subgroup of the International Olympic Committee (IOC) Consensus on REDs
Bibliographic record
Abstract
LEA, with a range of potential effects on both health and performance. However, there is variability in the outcomes of LEA exposure between and among individuals as well as the specific manifestations of REDs. We outline a framework for a 'systems biology' examination of the effect of LEA on individual body systems, with the eventual goal of creating an integrated map of body system interactions. We provide a template that systematically identifies characteristics of LEA exposure (eg, magnitude, duration, origin) and a variety of moderating factors (eg, medical history, diet and training characteristics) that could exacerbate or attenuate the type and severity of impairments to health and performance faced by an individual athlete. The REDs Physiological Model may assist the diagnosis of underlying causes of problems associated with LEA, with a personalised and nuanced treatment plan promoting compliance and treatment efficacy. It could also be used in the strategic prevention of REDs by drawing attention to scenarios of LEA in which impairments of health and performance are most likely, based on knowledge of the characteristics of the LEA exposure or moderating factors that may increase the risk of harmful outcomes. We challenge researchers and practitioners to create a unifying and dynamic physiological model for each body system that can be continuously updated and mapped as knowledge is gained.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".