Ovarian Function Suppression: A Deeper Consideration of the Role in Early Breast Cancer and its Potential Impact on Patient Outcomes: A Consensus Statement from an International Expert Panel
Bibliographic record
Abstract
It has been suggested that the benefit of adjuvant chemotherapy (CT) in premenopausal women with hormone receptor-positive (HR+), human epidermal growth factor receptor 2 negative (HER2-) early breast cancer may be related, at least in part, to CT-induced ovarian function suppression (OFS) in this subgroup of patients. Although this hypothesis has not been directly tested in large randomized clinical trials, the observations from prospective studies have been remarkably consistent in showing a late benefit of CT among the subgroup of patients who benefit (ie, women who were close to menopause). The hypothesis has important clinical implications, as it may be possible to spare the associated adverse effects of adjuvant CT in a select group of women with early breast cancer, in favor of optimizing OFS and endocrine therapy (ET), without compromising clinical outcomes. Such an approach has the added benefit of preserving the key quality of life outcomes in premenopausal women, particularly by preventing the irreversible loss of ovarian function that may result from CT use. For this reason, we convened an international panel of clinical experts in breast cancer treatment to discuss the key aspects of the available data in this area, as well as the potential clinical implications for patients. This article summarizes the results of these discussions and presents the consensus opinion of the panel regarding optimizing the use of OFS for premenopausal women with HR+, HER2- early breast 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 | 0.056 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.001 | 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".