Toxicity of Extended Adjuvant Therapy With Aromatase Inhibitors in Early Breast Cancer: A Systematic Review and Meta-analysis
Why this work is in the frame
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Bibliographic record
Abstract
Background: A number of randomized controlled trials (RCTs) have reported improvement in breast cancer outcomes from extending treatment with aromatase inhibitors (AIs) beyond the initial five years after diagnosis. However, the toxicity profile of extended AIs is uncertain. Methods: We identified RCTs that compared extended AIs to placebo or no treatment using MEDLINE and a review of abstracts from key conferences between 2013 and 2016. Odds ratios (ORs), 95% confidence intervals (CIs), absolute risks, and the number needed to harm (NNH) were computed for prespecified safety and tolerability outcomes including cardiovascular events, bone fractures, second cancers (excluding new breast cancer), treatment discontinuation for adverse events, and death without recurrence. All statistical tests were two-sided. Results: Seven trials comprising 16 349 patients met the inclusion criteria. Longer treatment with AIs was associated with increased odds of cardiovascular events (OR = 1.18, 95% CI = 1.00 to 1.40, P = .05, NNH = 122), bone fractures (OR = 1.34, 95% CI = 1.16 to 1.55, P < .001, NNH = 72), and treatment discontinuation for adverse events (OR = 1.45, 95% CI = 1.25 to 1.68, P < .001, NNH = 21). Longer treatment with AIs did not influence the odds of either second malignancy (OR = 0.93, 95% CI = 0.73 to 1.18, P = .56) or deaths without breast cancer recurrence (OR = 1.11, 95% CI = 0.90 to 1.36, P = .34). Conclusions: Extended treatment with AIs is associated with an increased risk of cardiovascular events and bone fractures. There is no statistically significant increase in deaths without breast cancer recurrence among patients receiving longer treatment with AIs. These data should be taken into account when considering extended adjuvant AIs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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