Pregnancy rate, maternal and neonatal outcomes among breast cancer survivors: A systematic review
Bibliographic record
Abstract
AIM: This study investigated the pregnancy rate, maternal and neonatal outcomes, and breast cancer (BC) recurrence status after pregnancy among BC survivors. DESIGN: A systematic review. METHODS: Electronic databases such as PubMed, Web of Science [WOS], Scopus, ScienceDirect, Google Scholar, and Scientific Information Database were systematically searched. The quality of included studies was evaluated using the Newcastle-Ottawa Scale (NOS). Observational studies reported the pregnancy rate, maternal and neonatal outcomes among reproductive-aged BC survivors, and the recurrence status of BC after pregnancy were eligible to include in this study. RESULTS: Of the 29 included studies, 13 studies were prospective cohorts or prospective multicenter or population-based cohorts, 14 studies were retrospective cohort or retrospective population-based cohort studies, and two studies were cross-sectional retrospective surveys or population-based descriptive studies. This systematic review showed that the pregnancy rate was estimated at 3.1%-48.5% among BC survivors who attempted to conceive. The most prevalent maternal outcomes of pregnancy were miscarriage (1.8%-33.3%) and induced abortion (5.0%-44%) as well as preterm birth (PTB) or very PTB (1.2%-21.1%), and twin birth (1.1%-38.8%) were the most prevalent neonatal outcomes occurring among BC survivors, respectively. In addition, most of the included studies indicated that pregnancy had no adverse effect on the status of BC recurrence among survivors. Surviving women can be encouraged and receive a carefully multidisciplinary approach regarding healthy pregnancy. No Patient or Public Contribution.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".