26 Combined hormonal contraceptive use is not protective against musculoskeletal conditions or injuries: A systematic review with data from 5-million women
Bibliographic record
Abstract
<h3>Introduction</h3> Half of young women start combined hormonal contraceptive (CHC) use for non-contraceptive reasons including ‘controlling’ their menstrual cycle to prevent injuries. These decisions should be evidence-based. This study assessed the association between CHC use and musculoskeletal tissue pathophysiology, injuries, or conditions. <h3>Materials and Methods</h3> After protocol registration, five databases were searched to 04–2022. Intervention and cohort studies assessing the association between new or ongoing use of CHC and musculoskeletal tissue pathophysiology, injury, or condition outcome in post-pubertal women were included. Record screening, data extraction, and risk-of-bias assessment were duplicated (blinded). Meta-analyses were not possible. Semi-quantitative syntheses followed a modified GRADE approach. <h3>Results</h3> Across 50 included studies, we assessed the effect of CHC use on 30 unique outcomes (75% bone-related). Serious risk-of-bias was judged present in 82% of studies, with 52% adequately adjusting for confounding. Meta-analyses were not possible due to heterogeneity in outcome methods, estimate statistics, and comparison conditions. Based on semi-quantitative synthesis, there is low certainty evidence that CHC use is associated with higher future fracture risk (RR 1.02–1.20), and total knee arthroplasty (RR 1.00–1.36). There is very low certainty evidence of unclear relationships between CHC use and a wide range of bone health outcomes. Evidence about the effect of CHC use on musculoskeletal tissues beyond bone, and the influence of use in adolescence versus adulthood is limited. <h3>Conclusion</h3> Given a paucity of high-certainty evidence that CHC use is protective against musculoskeletal pathophysiology, injury, or conditions, it is premature and inappropriate to prescribe CHC for these purposes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".