Reproductive and Pregnancy-Related Outcomes Among Adolescent and Young Adult female Cancer Survivors (15–39 years): A Nationwide Population-Based Study in South Korea
Bibliographic record
Abstract
Backgrounds: As survival rates among adolescent and young adult (AYA) female cancer patients improve, increasing numbers of survivors pursue pregnancy and childbirth. However, population-level data on reproductive outcomes and pregnancy-related complications among female cancer survivors remain limited. Methods: This study collected de-identified data from the South Korean National Health Insurance Service Database. Among 95,264 women aged 15–39 years newly diagnosed with cancer between 2007 and 2010, we identified 58,107 eligible survivors. This cohort was age matched 1:10 with 719,213 women without cancer. Reproductive outcomes were assessed in the full cohort, and pregnancy-related complications were evaluated among women with documented pregnancies. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using logistic regression. Results: Compared with matched controls, female cancer survivors had a higher risk of infertility (OR 1.25; 95% CI 1.21–1.28) and lower odds of pregnancy (OR 0.94; 95% CI 0.93 0.96) and childbirth (OR 0.81; 95% CI 0.78–0.84). Among women who became pregnant, cancer survivors experienced higher risks of ectopic pregnancy (OR 1.20; 95% CI 1.11–1.29), miscarriage or abortion (OR 1.23; 95% CI 1.14–1.32), intrauterine infection (OR 1.26; 95% CI 1.01–1.57), and preterm labor (OR 1.21; 95% CI 1.14–1.29) Conclusion: AYA female cancer survivors face increased risks of infertility and adverse pregnancy-related outcomes, including ectopic pregnancy, miscarriage, preterm labor, and preterm birth. These findings support the importance of specialized reproductive counselling and high-risk obstetric care tailored to the unique needs of female cancer survivors.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".