Gefitinib or Placebo in Combination with Tamoxifen in Patients with Hormone Receptor–Positive Metastatic Breast Cancer: A Randomized Phase II Study
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Increased growth factor signaling may contribute to tamoxifen resistance. This randomized phase II trial assessed tamoxifen plus placebo or the epidermal growth factor receptor inhibitor gefitinib in estrogen receptor (ER)-positive metastatic breast cancer. EXPERIMENTAL DESIGN: Patients with newly metastatic disease or recurred after adjuvant tamoxifen (stratum 1), or recurred during/after adjuvant aromatase inhibitor (AI) or after failed first-line AI (stratum 2), were eligible. Primary variables were progression-free survival (PFS; stratum 1) and clinical benefit rate (CBR; stratum 2). A 5% or more improvement in response variables with gefitinib was considered to warrant further investigation. Outcome was correlated with biomarkers measured on the primary tumor. RESULTS: In stratum 1 (n = 206), the PFS HR (gefitinib:placebo) was 0.84 (95% CI, 0.59-1.18; median PFS 10.9 versus 8.8 months). In the stratum 1 endocrine therapy-naïve subset (n = 158) the HR was 0.78 (95% CI, 0.52-1.15), and the prior endocrine-treated subgroup (n = 48) 1.47 (95% CI, 0.63-3.45). In stratum 1, CBRs were 50.5% with gefitinib and 45.5% with placebo. In stratum 2 (n = 84), CBRs were 29.2% with gefitinib and 31.4% with placebo. Biomarker analysis suggested that in stratum 1 there was greater benefit with gefitinib in patients who were ER-negative or had lower levels of ER protein. CONCLUSIONS: In stratum 1, the improved PFS with gefitinib plus tamoxifen met the protocol criteria to warrant further investigation of this strategy. In stratum 2, there was a numerical disadvantage for gefitinib; additional investigation after AI therapy is not warranted. Studies of predictive biomarkers are needed to subset appropriate patients.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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