Female Oncofertility and Immune Checkpoint Blockade in Melanoma: Where Are We Today?
Bibliographic record
Abstract
The incidence of melanoma among young adults has risen, yet mortality has declined annually since the introduction of immune checkpoint inhibitors (ICI). The utilization of peri-operative ICI has significantly altered the treatment landscape in melanoma, with PD-1 inhibitors showing promising efficacy in improving relapse-free survival rates in high-risk stage II-III disease. With the increasing use of ICI, secondary concerns have emerged regarding the impact of cancer drugs on fertility and reproductive health among women of childbearing potential, especially in early-stage cancer settings. The exclusion of pregnant women from trials contributes to limited human data and clinical uncertainties, such as maternal and fetal toxicities related to ICI exposure during pregnancy, as well as the value of fertility preservation before ICI therapy. Uncertainty persists regarding pregnancy post-adjuvant immunotherapy, given the potential detrimental effects of hormonal and immunological changes during pregnancy on melanoma relapse. There is additional uncertainty about whether pregnancy-associated melanoma (PAM) represents a distinct disease entity that warrants tailored management compared to non-pregnant cases. Our review aims to give an overview of oncofertility practices among female melanoma patients after immunotherapy. We also focus on the literature gap in the published evidence and synthesize summaries regarding ICI toxicities on reproductive health and fetal development, pregnancy planning, and recurrence risks after melanoma treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".