Patient and Physician Perceptions on Continuing Aromatase Inhibitors beyond the 5-Year Mark
Bibliographic record
Abstract
Aromatase inhibitors (AIs) have been shown to improve disease-free survival and in certain cases, overall survival in the treatment of postmenopausal women with hormone receptor positive early breast cancer. Trials are ongoing to determine if AI therapy should be continued for patients who have already completed 5 years of AI treatment. The objective of this study was to assess the minimum disease-free and overall survival benefit acceptable to physicians and to women undergoing AI therapy to continue treatment beyond 5 years. A self-administered survey was completed by women with stage I-III breast cancer, who were undergoing adjuvant AI therapy for at least 1 year. The survey assessed relevant cancer-related, treatment, social and comorbid factors, and FACT-ES (V4). Minimum acceptable treatment benefit was denoted as a percentage decrease in cancer recurrence risk, and percentage increase in survival at 5 years. Medical oncologists (MOs) treating breast cancer across Canada were also surveyed. A total of 153 patients were surveyed; median age was 60, 51% had node-negative disease, 89% had prior radiation therapy, 61% had prior chemotherapy, and 59% had prior tamoxifen therapy. Mean duration of AI therapy was 31 months. Approximately 30% of women required a 5-year survival benefit of less than 1%, and 27.5% needed a decrease in risk of recurrence of less than 1% to continue an AI beyond the initial 5 years. In contrast, 45% of the 40 surveyed MOs required a 5-year survival benefit of at least 1-2%, and 37.5% preferred a decrease in recurrence risk of 2-5% to prescribe an AI for an additional 5 years. There was a significant correlation between severity of endocrine symptoms experienced on AIs and an increased minimum survival benefit required for women to continue therapy (r = 0.18, p = 0.036). Patients were willing to continue on AIs for smaller treatment benefits than physicians would prefer to prescribe them beyond 5 years. Patient preference to continue on AIs correlated somewhat to the severity of AI-related side effects.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".