High-Grade Serous Ovarian Cancer during Pregnancy: From Diagnosis to Treatment
Bibliographic record
Abstract
BACKGROUND: Due to the rarity of ovarian cancer diagnosed during pregnancy, the literature on the treatment of subtypes of epithelial ovarian cancer in pregnancy is sparse. The aim of our review was to analyze cases of high-grade serous ovarian cancer in pregnancy. METHODS: The PubMed and Scopus databases were searched for relevant articles published in English between January 2000 and December 2023. The references of all the relevant reviews found were also checked to avoid omitting eligible studies. Information on the all retrieved cases was extracted and reviewed in detail. The most important detail was the subtype of high-grade serous ovarian cancer, which was referred to as serous adenocarcinoma (grade 2 or grade 3) in older cases. RESULTS: We found eleven cases with relevant details of high-grade serous ovarian cancer diagnosed in pregnancy. Despite the small number of cases we found, our study demonstrated the importance of an accurate initial vaginal ultrasound at the first examination in pregnancy and the safety of diagnostic surgery and chemotherapy in pregnancy. CONCLUSIONS: There have not been long-term follow-ups of patients' oncologic and obstetric outcomes. As patients should be comprehensively informed, more detailed case reports or series with longer follow-up periods are needed.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".