Clinical Management of Ovarian Function Suppression in Premenopausal Women With Breast Cancer: A Survey of Members of ASCO
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Ovarian function suppression (OFS) with gonadotropin-releasing hormone agonists (GnRHas) is a standard of care for premenopausal patients with high-risk stage II/III hormone receptor-positive breast cancer (BC). Practical guidance on the optimal choice of GnRHa, timing, schedule, and monitoring is limited. Our aim was to determine how oncologists use OFS in routine care. METHODS: We designed a questionnaire to determine the choice of GnRHa, schedule, duration, initiation, use of bone modifiers, and monitoring of estradiol (E2). The questionnaire was sent to oncologists treating BC, in practice for >1 year and participating in the ASCO Research Survey Pool (RSP). It was also forwarded by investigators to oncologists meeting these criteria. The survey was open between November 14, 2023, and January 5, 2024. RESULTS: Of 996 oncologists participating in the ASCO RSP, 178 (18%) completed the survey. An additional 56 oncologists contacted by investigators responded. Respondents were from the United States (57%), Asia (15%), and Europe (14%). Goserelin (54%) and leuprolide (39%) were the most frequently used GnRHas and were administered once every month by 46%. Approaches to starting GnRHas were varied. Most continued them for the duration of aromatase inhibitor therapy (57%). Estradiol monitoring was performed regularly, sometimes, or never by 43%, 27%, and 27%, respectively. The E2 assays used were standard (65%), ultrasensitive (16%), and unknown (14%). Interpreting E2 assay results were considered difficult by 55%; however, 62% of oncologists changed treatment on the basis of them. A total of 92% of respondents would like ASCO guidance on the practical use of OFS. CONCLUSION: Considerable practice variation exists for similar clinical scenarios in OFS administration. Respondents would welcome ASCO guidance on all aspects of OFS.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it