Abstract S2-7: Toxicity of Adjuvant Endocrine Therapy in Postmenopausal Breast Cancer Patients
Bibliographic record
Abstract
Abstract Purpose: To determine differences in serious adverse events (AEs) associated with aromatase inhibitors (AIs) compared with tamoxifen, and to explore whether these AEs might be ameliorated by switching to AIs after 2-3 years of tamoxifen. Materials and Methods: Published data from trials contributing to the Aromatase Inhibitor Overview Group were included in a meta-analysis. Odds ratios (OR), 95% confidence intervals (CI) and absolute risks (number needed to treat [NNT] associated with one AE) were computed for serious AEs Results: Any duration of AIs was associated with a higher probability of developing cardiovascular disease (OR 1.20, p=0.01; NNT 143) and bone fractures (OR 1.48, P<0.00001; NNT 34), but a reduced probability of venous thrombosis (OR 0.53, P<0.00001; NNT 67) and endometrial carcinoma (OR 0.32, P<0.00001; NNT 200). The risks of individual serious AEs were similar for upfront AIs and for switching to AIs after tamoxifen. However, compared with 5 years of tamoxifen, there was a non-significant trend towards increased death without recurrence with upfront AIs (OR 1.12, p=0.16), but lower with switching to AIs after tamoxifen (OR 0.74, p=0.03). Conclusion: Treatment with AIs is associated with a statistically significant increase in cardiovascular risk, which is of similar magnitude to the risk of venous thrombosis and endometrial cancer with 5-years of tamoxifen. While switching to AIs does not appear to reduce the risk for the development of serious AEs when compared to upfront use of AIs, fewer deaths unrelated to breast cancer occur with switching than with upfront strategies. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr S2-7.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.015 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".