Fertility Preservation in Reproductive-Age Women Facing Gonadotoxic Treatments
Bibliographic record
Abstract
BACKGROUND: Advancements in the treatments for cancer and autoimmune and other hematologic conditions continue to improve survival and cure rates. Despite those changes, various gonadotoxic agents and other treatments can still compromise the future fertility of many women. Progress in medical and surgical reproductive technologies has helped to offset the reproductive consequences of the use of gonadotoxic therapies, and allows for future fertility and normal pregnancy. METHODS: A review of the literature was performed to outline the pathophysiology of gonadotoxicity from various treatments. The success of fertility preservation, fertility sparing, and cryopreservation options are reviewed. Barriers and facilitators to referral and oncofertility treatment in Canada are also outlined. RESULTS: According to the quality of the evidence, recommendations are made for fertility assessment, patient referral, cryopreservation, and other assisted reproductive technologies. CONCLUSIONS: To ensure ongoing fertility in women undergoing gonadotoxic treatments, assisted reproductive technologies can be combined with a multidisciplinary approach to patient assessment and referral.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".