Methodology for studying Relative Energy Deficiency in Sport (REDs): a narrative review by a subgroup of the International Olympic Committee (IOC) consensus on REDs
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Bibliographic record
Abstract
In the past decade, the study of relationships among nutrition, exercise and the effects on health and athletic performance, has substantially increased. The 2014 introduction of Relative Energy Deficiency in Sport (REDs) prompted sports scientists and clinicians to investigate these relationships in more populations and with more outcomes than had been previously pursued in mostly white, adolescent or young adult, female athletes. Much of the existing physiology and concepts, however, are either based on or extrapolated from limited studies, and the comparison of studies is hindered by the lack of standardised protocols. In this review, we have evaluated and outlined current best practice methodologies to study REDs in an attempt to guide future research. This includes an agreement on the definition of key terms, a summary of study designs with appropriate applications, descriptions of best practices for blood collection and assessment and a description of methods used to assess specific REDs sequelae, stratified as either Preferred , Used and Recommended or Potential . Researchers can use the compiled information herein when planning studies to more consistently select the proper tools to investigate their domain of interest. Thus, the goal of this review is to standardise REDs research methods to strengthen future studies and improve REDs prevention, diagnosis and care.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it