Application of the IOC Relative Energy Deficiency in Sport (REDs) Clinical Assessment Tool version 2 (CAT2) across 200+ elite athletes
Bibliographic record
Abstract
OBJECTIVE: This cross-sectional retrospective and prospective study implemented the 2023 International Olympic Committee Relative Energy Deficiency in Sport (REDs) Clinical Assessment Tool version 2 (CAT2) to determine the current severity of REDs (primary outcome) and future risk of bone stress injuries (BSI, exploratory outcome) in elite athletes. METHODS: Female (n=143; 23.3±4.3 years) and male (n=70; 23.1±3.7 years) athletes (performance tier 3 (52%), tier 4 (36%), tier 5 (12%)) participated in a baseline CAT2 (with minor modifications) assessment, including a self-report questionnaire (menstrual function (females), BSI, Eating Disorder Examination Questionnaire (EDE-Q)), bone mineral density (BMD via DXA) and fasted blood analysis (triiodothyronine (T3), testosterone, cholesterol). Athletes were assigned a green, yellow, orange or red light via CAT2. The prospective risk of new self-report of physician-diagnosed BSI was assessed over a subsequent 6-24 months. RESULTS: REDs prevalence was 55% green, 36% yellow, 5% orange and 4% red light. The CAT2 identified a greater prevalence of amenorrhoea and BSI and lower T3, testosterone and BMD (p<0.01) in red, orange and yellow (those with REDs) versus green light. ORs for a prospective self-reported BSI (majority physician diagnosed) were greater in orange vs green (OR 7.71, 95% CI (1.26 to 39.83)), in females with severe amenorrhoea (OR 4.6 (95% CI 0.98 to 17.85)), in males with low sex drive (OR 16.0 (95% CI 4.79 to 1038.87)), and athletes with elevated EDE-Q global scores (OR 1.45 (95% CI 0.97 to 1.97)). CONCLUSION: The CAT2 has high validity in demonstrating current severity of REDs, with increased future risk of self-reported BSI in athletes with a more severe REDs traffic light category.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".