Silent Struggles: Relative Energy Deficiency in Sport (REDs) in Female Athletes
Bibliographic record
Abstract
Relative Energy Deficiency in Sport (REDs) is a condition caused by prolonged periods of reduced energy intake relative to expenditure, specifically in sport. Although estimates of prevalence vary, most of the athletic population likely suffers from REDs. This interview-based project examined the state of REDs knowledge, awareness, and practices in female athletes and those working with them. Female athletes were the primary focus given their relative absence from existing research. Results from this project suggest that most cases of REDs are caused by unintentional nutrient restriction with parents, society, and social media spreading poor nutrition information and behaviors that athletes eventually adopt. Identifying and treating REDs is complicated by the need for cooperation from athletes, who may be unaware of their inadequate fueling practices or are intentionally hiding their restrictive behaviors. Obtaining an official diagnosis is often a complex and lengthy process, with many healthcare professionals working together to exclude other potential medical conditions. Overall, this study suggests that at-large REDs education is necessary for athletes and those working with them. Additionally, implementing REDs screening practices, employing dietitians, and making support and similar resources available at sports institutions may decrease the prevalence of REDs and expedite the identification and treatment process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".