A global perspective on the use of aromatase inhibitors in early-stage breast cancer.
Bibliographic record
Abstract
Abstract Abstract #1144 Background: Aromatase inhibitors (AIs) are considered to be the standard of care for the adjuvant treatment of postmenopausal patients with hormone-receptor positive (HR+) early breast cancer (EBC). However, there is a lack of data on the use of these drugs in the non-trial setting and in regional practice around the world. Methods: This survey examined the use of endocrine therapies in breast cancer by physicians in 7 countries. From July 27 to October 8, 2007, 462 physicians in the United States, Germany, the United Kingdom, France, Spain, Italy, and Japan were surveyed, and information was collected via an internet Web site. Data included the physicians' reported activity during the last month, AI profiles, and patient profiles according to treatment stage. Results: Surveyed were 381 oncologists, 14 gynecologists, 36 surgeons, and 31 mammary gland clinicians. Physician perceptions were similar to the actual patient data. Half of all women being treated for breast cancer were HR+ and postmenopausal (less in Japan: 38%), and almost all of those patients (80-95%) were receiving endocrine therapy for the adjuvant treatment of EBC. AIs were the most common hormone agents used in the initial adjuvant setting of postmenopausal women with HR+ EBC across all countries surveyed (55-93%; median 88%). An upfront AI strategy was the most common practice overall, particularly in Japan, the United States, and France (89%, 86%, and 86%, respectively). Initial tamoxifen therapy was used predominantly in Germany (36%), the United Kingdom (51%), and Italy (55%). However, in the United Kingdom and Germany, the intent in nearly half of these patients was to switch to an AI following 2 years of tamoxifen therapy. The rates at which the 3 AIs were prescribed for different types of adjuvant treatment were generally consistent across countries. Guidelines were identified as a major decision-making factor in all countries. Conclusion: There are variations in practice across the surveyed countries despite the presence of international guidelines, eg, the American Society of Clinical Oncology and St. Gallen 2007. These practice trends may reflect the various guidelines in each region. Differences in treatment are particularly seen in Europe, which may reflect the number of varying guidelines and less descriptive nature of the guidelines. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 1144.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".