Health Outcomes after Pregnancy in Elite Athletes: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
PURPOSE: This study aimed to evaluate postpartum maternal health and training outcomes of females who were competing or training as elite athletes before or during pregnancy. METHODS: Online databases were searched up to August 26, 2020. Studies of any design and language were eligible if they contained information on the relevant population (postpartum athletes [any period after pregnancy]), exposure (engaged in the highest level of sport immediately before or during pregnancy), comparators (sedentary/active controls), and outcomes: maternal (breastfeeding initiation and duration, postpartum weight retention or loss, bone mineral density, low back or pelvic girdle pain, incontinence [prevalence or severity of stress, urge or mixed urinary incontinence, fecal incontinence], injury, anemia, diastasis recti, breast pain, depression, anxiety) and training (<6 wk time to resume activity, training volume or intensity, performance level). RESULTS: Eleven studies (n = 482 females, including 372 elite athletes) were included. We identified "very low" certainty evidence demonstrating a higher rate of return to sport before 6 wk postpartum among elite athletes compared with nonelite athletes (n = 145, odds ratio = 6.93, 95% confidence interval = 2.73-17.63, I2 = 11). "Very low" certainty evidence from three studies (n = 179) indicated 14 elite athletes obtained injuries postpartum (7 stress fractures, 9 "running injuries"). "Very low" certainty evidence from five studies (n = 262) reported that 101 (40.5%) elite athletes experienced improved performance postpartum. CONCLUSION: Compared with controls, "very low" quality evidence suggests that elite athletes return to physical activity early in the postpartum period and may have an increased risk of injury. Additional high-quality evidence is needed to safely guide return to sport of elite athletes in the postpartum period.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".