Responding to Relative Energy Deficiency in Sport (REDs): a multidisciplinary care pathway for safe return to sport
Bibliographic record
Abstract
Abstract: Introduction: Relative energy deficiency in sport (REDs) is a syndrome of impaired physiological and/or psychological functioning caused by prolonged/severe low energy availability (LEA). The consequences associated with LEA may be challenging to manage and requires a team approach. As no guidelines currently exist on how to engage a multidisciplinary team, the objective of this study was to propose a team approach and clinical care pathway for clinicians involved in REDs care. Methods: Five expert clinicians from different perspectives [Physical Therapist (PT), Sport Medicine Physician (MD), Endocrinologist (Endo), Registered Dietitian (RD) and Sport Psychiatrist (Psych)] determined an optimal team approach to treatment, management and referral pathways for athletes presenting with REDs. Results: A clinical care pathway should follow initial risk stratification, diagnosis, and a team approach to management and return to sport (RTS). Each member of the clinical team plays different but important roles at each step, and relevant referrals should be initiated early and often. For instance, a referral to a PT may be initiated for concomitant injury management, and an RD for nutritional support. Furthermore, referral to an MD/Endo is warranted for diagnosis and ruling out other medical conditions, and referral to Psych for psychological factors that interfere with recovery. Forming and maintaining a clinical alliance between all practitioners is essential during the RTS phase to ensure consistency of messaging and approach. Conclusion: Each member of the clinical management team plays a unique yet integral role in guiding the athlete through the phases of REDs identification, management, and recovery. Engaging a comprehensive multidisciplinary team spanning physical and mental health as well as nutrition is recommended to optimize athlete health.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".