No association between abortion and risk of breast cancer among nulliparous women
Bibliographic record
Abstract
BACKGROUND: Various epidemiological studies have demonstrated the association between abortion and risk of breast cancer among nulliparous women; however, results remain inconclusive. This meta-analysis assessed the association based on previous studies. METHODS: PubMed, EMBase, China National Knowledge Infrastructure, Chongqing VIP, and Wanfang databases were searched for relevant articles until February 2018. In this meta-analysis, fixed-effects models were used to estimate the combined effect size and the corresponding 95% confidence interval (CI). All statistical data were analyzed using STATA 12.0. RESULTS: A total of 14 articles consisting of 6 cohort studies and 8 case-control studies were included in this review. All articles were of high quality, as determined based on the Newcastle Ottawa Scale assessment. The combined risk ratio (RR) indicated no significant association between abortion and breast cancer among nulliparous women (RR = 1.023, 95%CI = 0.938-1.117; Z = 0.51, P = .607). Subgroup analyses revealed no significant associations between risk of breast cancer and induced abortion or between risk of breast cancer and spontaneous abortion (SA) among nulliparous women (RR = 1.008, 95% CI = 0.909-1.118 and RR = 1.062, 95%CI = 0.902-1.250, respectively). Neither 1 nor >2 abortions increased the risk of breast cancer among nulliparous women. Sensitivity analysis showed that our results were reliable and stable. CONCLUSION: Current evidence based on epidemiological studies showed no association between abortion and risk of breast cancer among nulliparous women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.017 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".