Do adjuvant aromatase inhibitors increase the cardiovascular risk in postmenopausal women with early breast cancer
Bibliographic record
Abstract
BACKGROUND: Despite the advantages from using aromatase inhibitors (AIs) compared with tamoxifen for early breast cancer, an unexpectedly greater number of grade 3 and 4 cardiovascular events (CVAE) (as defined by National Cancer Institute of Canada-Common Toxicity Criteria [version 2.0] was demonstrated. METHODS: Phase 3 randomized clinical trials (RCTs) comparing AI with tamoxifen in early breast cancer were considered eligible for this review. The event-based risk ratios (RRs) with 95% confidence intervals (95% CIs) were derived, and a test of heterogeneity was applied. Finally, absolute differences (ADs) in event rates and the number of patients needed to harm 1 patient (NNH) were determined. RESULTS: Seven eligible RCTs (19,818 patients) reported CVAE results. When considering all RCTs, the AD of the primary endpoint (CVAE) between the 2 arms (0.52%), tamoxifen versus AI, was statistically significant (RR, 1.31; 95% CI, 1.07-1.60; P= .007). This translated into an NNH value of 189 patients; when only third-generation AIs were considered, the difference (0.57%) remained significant (RR, 1.34; 95% CI, 1.09-1.63; P= .0038). Thromboembolic events were significantly more frequent in the tamoxifen arm, regardless of the strategy adopted (RR, 0.53; 95% CI, 0.42-0.65; P< .0001), without significant heterogeneity (P= .21). An AD of 1.17% and an NNH value of 85 patients were observed. CONCLUSIONS: According to the results from this meta-analysis, the risk of grade 3 and 4 CVAEs in patients who were receiving AIs was higher compared with the risk in patients who were receiving tamoxifen, and the difference reached statistical significance. However, the AD was relatively low, and from 160 to 180 patients had to be treated to produce 1 event.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".