New approach to identifying elite winter sport athletes’ risk of relative energy deficiency in sport (REDs)
Bibliographic record
Abstract
Objectives: Relative energy deficiency in sport (REDs) is a syndrome resulting from problematic low energy availability (LEA). Low areal bone mineral density (aBMD) is a primary indicator of LEA, measured by dual X-ray absorptiometry (DXA). High-resolution peripheral quantitative CT (HR-pQCT) is an advanced imaging device that provides measures of volumetric BMD (vBMD), bone microarchitecture, geometry and strength. This study aimed to assess the prevalence of REDs in elite winter sport athletes and to observe the associations in bone parameters using HR-pQCT in athletes identified as at-risk or not at-risk of REDs. Methods: Participants included 101 elite athletes (24.1±4.4 SD years; 52% female). The REDs Clinical Assessment Tool (CAT2) was used to determine REDs risk. HR-pQCT scans of the non-dominant radius and left tibia were analysed on REDs risk grouping. Results: 17 athletes (17%; 71% female) were at-risk based on the REDs CAT2. After covarying for lean mass, OR suggested a higher likelihood of REDs risk classification for athletes with low cortical thickness, cortical area, total vBMD and bone strength. Conclusions: Impaired total vBMD, bone strength and cortical bone parameters were approximately twice as likely (OR: 1.9-3.0) in athletes at-risk of REDs. Results agree with the consensus statement that HR-pQCT may identify impaired bone health in athletes at-risk of REDs. Future directions should use HR-pQCT to explore REDs risk longitudinally, using bone change over time, as this may provide greater insight. Using advanced imaging to explore REDs risk in a population of winter high-performance athletes is novel.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".