Systematic literature review and trial-level meta-analysis of aromatase inhibitors vs tamoxifen in patients with HR+/HER2− early breast cancer
Bibliographic record
Abstract
BACKGROUND: Current standard of care for patients with HR+/HER2- early breast cancer (EBC) includes adjuvant endocrine therapy with an aromatase inhibitor (AI) or tamoxifen (TAM). We present a trial-level meta-analysis on efficacy of AI vs TAM in patients with HR+/HER2- EBC. METHODS: A systematic literature review was conducted using key medical literature databases (eg, PubMed; inception to October 2023) and data from conferences (to December 2023). Phase 3 randomized controlled trials (RCTs) that had ≥80 % of patients with HR+/HER2- EBC (or available subgroup data) and reported a disease-free survival (DFS) hazard ratio for AI vs TAM were included in the meta-analysis, regardless of menopausal status and ovarian function suppression (OFS) use. The generic invariance method was used to calculate a pooled effect estimate of DFS hazard ratios and 95 % CIs. A base-case analysis (all RCTs) and scenario analyses for NSAI-only, premenopausal, and postmenopausal RCTs were conducted. RESULTS: Five RCTs were identified for inclusion in the meta-analysis. In the base-case analysis, DFS significantly favored AI ± OFS vs TAM ± OFS (pooled hazard ratio, 0.68; 95 % CI, 0.61-0.76; P < .0001). Results from scenario analyses were consistent with the base case; NSAI-only (pooled hazard ratio, 0.68; 95 % CI, 0.59-0.78; P < .0001), premenopausal (pooled hazard ratio, 0.65; 95 % CI, 0.56-0.76; P < .0001), and postmenopausal (pooled hazard ratio, 0.72; 95 % CI, 0.61-0.86; P = .001) RCTs favored AI ± OFS over TAM ± OFS. CONCLUSIONS: This trial-level meta-analysis demonstrated a significant DFS benefit with AI vs TAM for patients with HR+/HER2- EBC, which was more pronounced in premenopausal women.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".