Oral contraceptives use and breast cancer risk: a systematic review and meta-analysis of prospective cohort studies
Bibliographic record
Abstract
Objectives Controversy persists regarding the association between oral contraceptives (OCs) use and breast cancer risk, with inconsistent findings from prior cohort studies. This study aimed to synthesise evidence on OCs use and breast cancer risk, including dose-response relationships.Methods Databases (PubMed, Embase, Web of Science) were searched through March 2025 for related prospective cohort studies. Eligibility criteria included female participants without baseline breast cancer, comparison of ever OCs users vs. never users, reported hazard ratio (HR) with 95% confidence interval (CI), and at least 5 years of follow-up. Study quality was assessed using the Newcastle-Ottawa Scale. Random-effects model was used to pool effect sizes, and a two-stage dose-response meta-analysis evaluated associations per decade of OCs use. Sensitivity analyses and publication bias tests were conducted.Results Sixteen studies (6,390,250 women; 104,070 breast cancer cases) were included. Pooled HR for OCs users vs. non-users was 1.03 (95% CI: 0.99, 1.07), with a 95% prediction interval (PI): 0.91, 1.16. Sensitivity analyses confirmed consistency, and no publication bias was detected for sixteen studies. Initial dose-response meta-analysis (6 studies) showed a non-significant 6% HR increase per decade use (exp[coefficient] = 1.06, 95% CI: 0.97, 1.16), with a 95% PI: 0.80, 1.41. Excluding one study by Dumeaux et al. revealed a significant 11% increase (p < 0.01).Conclusion Based on sixteen studies, OCs ever-users were not significantly associated with breast cancer risk. Based on six limited studies, no strong dose-response relationship that warrants special attention has been identified.
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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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".