Cancer and fertility management: <scp>FIGO</scp> best practice advice
Bibliographic record
Abstract
Cancer diagnoses in patients of reproductive age require balancing urgent oncological treatment with the need to preserve fertility. This FIGO Best Practice Advice outlines key considerations for fertility management in this population given the rising cancer incidence among young women and the reproductive risks posed by cancer treatments. The guidance evaluates the impact of chemotherapy, radiotherapy, surgery, and emerging therapies-such as targeted agents and immunotherapies-on gonadal function and fertility. Established fertility preservation methods, including oocyte/embryo cryopreservation, sperm banking, and ovarian tissue freezing, are detailed alongside barriers to their adoption, such as cost and limited access. Early collaborative counseling with oncologists and fertility specialists is central to addressing timelines, psychological impacts, and priorities. Post-treatment pathways, including assisted reproduction and surrogacy, are also explored. The guidance stresses the importance of integrating fertility-sparing interventions and fertility preservation into cancer care while advocating for equitable access to resources. Further research is needed to refine preventive interventions, evaluate long-term outcomes, and expand options for survivors globally. By prioritizing fertility preservation within oncological care, healthcare providers can better support the holistic needs of young individuals facing cancer.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.115 | 0.048 |
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".