Primary, secondary and tertiary prevention of Relative Energy Deficiency in Sport (REDs): a narrative review by a subgroup of the IOC consensus on REDs
Bibliographic record
Abstract
Relative Energy Deficiency in Sport (REDs) is common among female and male athletes representing various sports at different performance levels, and the underlying cause is problematic low energy availability (LEA). It is essential to prevent problematic LEA to decrease the risk of serious health and performance consequences. This narrative review addresses REDs primary, secondary and tertiary prevention strategies and recommends best practice prevention guidelines targeting the athlete health and performance team, athlete entourage (eg, coaches, parents, managers) and sport organisations. Primary prevention of REDs seeks to minimise exposure to and reduce behaviours associated with problematic LEA. Some of the important strategies are educational initiatives and de-emphasising body weight and leanness, particularly in young and subelite athletes. Secondary prevention encourages the early identification and management of REDs signs or symptoms to facilitate early treatment to prevent development of more serious REDs outcomes. Recommended strategies for identifying athletes at risk are self-reported screening instruments, individual health interviews and/or objective assessment of REDs markers. Tertiary prevention (clinical treatment) seeks to limit short-term and long-term severe health consequences of REDs. The cornerstone of tertiary prevention is identifying the source of and treating problematic LEA. Best practice guidelines to prevent REDs and related consequences include a multipronged approach targeting the athlete health and performance team, the athlete entourage and sport organisations, who all need to ensure a supportive and safe sporting environment, have sufficient REDs knowledge and remain observant for the early signs and symptoms of REDs.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".