Outcome of Patients with Pregnancy during or after Breast Cancer: A Review of the Recent Literature
Bibliographic record
Abstract
BACKGROUND: An increasing number of young women are delaying childbearing; hence, more are diagnosed with breast cancer (bca) before having a family. No clear recommendations are currently available for counselling such a population on the safety of carrying a pregnancy during bca or becoming pregnant after treatment for bca. METHODS: Using a Web-based search of PubMed we reviewed the recent literature about bca and pregnancy. Our objective was to report outcomes for patients diagnosed with bca during pregnancy, comparing them with outcomes for non-pregnant women, and to evaluate prognosis in women diagnosed with and treated for bca who subsequently became pregnant. RESULTS: "Pregnancy and bca" should be divided into two entities. Pregnancy-associated bca tends to be more aggressive and advanced in stage at diagnosis than bca in control groups; hence, it has a poorer prognosis. With respect to pregnancy after bca, there is, despite the bias in reported studies and meta-analyses, no clear evidence for a different or worse disease outcome in bca patients who become pregnant after treatment compared with those who do not. CONCLUSIONS: Pregnancy-associated bca should be treated as aggressively as and according to the standards applicable in nonpregnant women; pregnancy after bca does not jeopardize outcome. The guidelines addressing risks connected to pregnancy and bca lack a high level of evidence for better counselling young women about pregnancy considerations and preventing unnecessary abortions. Ideally, evidence from large prospective randomized trials would set better guidelines, and yet the complexity of such studies limits their feasibility.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| 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) | 0.003 | 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".