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 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 | 0.027 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.046 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".