Boston Marathon athlete performance outcomes and intra-event medical encounter risk associated with low energy availability indicators
Bibliographic record
Abstract
OBJECTIVE: To determine the association between survey-based self-reported problematic low energy availability indicators (LEA-I) and race performance and intra-event medical encounters during the Boston Marathon. METHODS: 1030 runners who were registered for the 2022 Boston Marathon completed an electronic survey (1-4 weeks pre-race) assessing LEA-I, training and medical history. De-identified survey data were linked to event wearable timing chips and medical encounter records. LEA-I was defined as: an elevated Eating Disorder Examination Questionnaire score, elevated Low Energy Availability (LEA) in Females Questionnaire score, LEA in Males Questionnaire with a focus on gonadal dysfunction score and/or self-report of diagnosed eating disorder/disordered eating. RESULTS: The prevalence of LEA-I was 232/546 (42.5%) in females and 85/484 (17.6%) in males. Athletes without LEA-I (non-LEA-I) achieved significantly better race times versus those with LEA-I (accounting for demographic and anthropomorphic data, training history and marathon experience), along with better division finishing place (DFP) mean outcomes (women's DFP: 948.9±57.6 versus 1377.4±82.9, p<0.001; men's DFP: 794.6±41.0 versus 1262.4±103.3, p<0.001). Compared with non-LEA-I athletes, LEA-I athletes had 1.99-fold (95% CI: 1.15 to 3.43) increased relative risk (RR) of an intra-event medical encounter of any severity level, and a 2.86-fold increased RR (95% CI:1.31 to 6.24) of a major medical encounter. CONCLUSION: This is the largest study to link LEA-I to intra-event athletic performance and medical encounters. LEA-I were associated with worse race performance and increased risk of intra-event medical encounters, supporting the negative performance and medical risks associated with problematic LEA-I in marathon athletes.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".