Fertility preservation and pregnancy outcome after malignancy
Bibliographic record
Abstract
PURPOSE OF REVIEW: The overall survival and cure rates of patients with childhood and adult malignancies have improved dramatically, but cancer treatment can be associated with diminished reproductive potential. However, research on the preservation of fertility in these patients has given patients new options. This article discusses the mechanisms of reproductive failure after cancer therapy and the currently available fertility preservation strategies. RECENT FINDINGS: Ovarian transposition is still a viable option if radiotherapy is to be used alone. Modifications in assisted reproductive technology that decrease peak estradiol levels are ideal for breast cancer survivors. Embryo freezing technology offers excellent pregnancy rates. Oocyte freezing is available for women without a partner, but there is more limited experience with this technique. Understanding the concepts of graft function after the autotransplantation of frozen-thawed ovarian tissue has resulted in great strides in the technical requirements for success. SUMMARY: Gonadotropin-releasing hormone analogues are the only available medical protection means for gonadotoxic chemotherapy. Assisted reproductive technology offers excellent results, but the protocols require a delay in implementing chemotherapy. Despite recent reports of embryo development after the transplantation of cryopreserved-thawed ovarian tissue, clinical experience is limited and the technique remains experimental.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".