Cancer in pregnancy: <scp>FIGO</scp> Best practice advice and narrative review
Bibliographic record
Abstract
Cancer during pregnancy is relatively rare. The incidence is underestimated due to the lack of international registries covering both high-income and low- and middle-income countries, and is expected to rise with increasing maternal age and increasing global adoption of cell-free DNA testing for aneuploidy. Physiological changes during pregnancy often make the diagnosis challenging and delayed. Lack of experience and knowledge about this condition may also contribute to late diagnosis, suboptimal management, and occasionally inadvertent fetal and/or maternal harm. The principles of cancer management in pregnancy for most cancer types do not differ significantly from the non-pregnant population. The impact of investigations for diagnosis and staging, risks of surgery, systemic chemotherapy, and/or radiotherapy on fetal well-being and preterm birth need to be considered for treatment and management planning, in addition to maternal wishes. Working in a multidisciplinary setting, ideally with medical and radiation oncologists, surgeons, radiologists, cancer specialist nurses, geneticists, psychologists, teratologists, and clinical pharmacologists, obstetricians, obstetric physicians, neonatologists, and experienced nursing and midwifery staff helps provide optimal care for the woman. This best practice advice aims to provide recommendations on the diagnosis and management of cancer in pregnancy, which can be adopted in all resource settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".