Estrogen Receptor Expression and Docetaxel Efficacy in Patients with Metastatic Breast Cancer: A Pooled Analysis of Four Randomized Trials
Bibliographic record
Abstract
BACKGROUND: Differences in the efficacy of various chemotherapies in patients with estrogen receptor (ER)(+) metastatic breast cancer are not well understood. In the present study, we assessed the efficacy of docetaxel in patients with metastatic breast cancer according to ER expression. METHODS: The efficacy of docetaxel in terms of the response rate and progression-free survival (PFS) time was analyzed according to ER expression in four randomized trials comparing a docetaxel-based regimen with a nontaxane regimen that included a total of 1,631 patients. The odds ratio for tumor response was estimated with logistic regression and a hazard ratio (HR) for PFS was estimated with Cox proportional hazards models. FINDINGS: ER expression was assessable in 1,037 patients included in these trials (64%). ER was expressed in 601 tumors (58%). Docetaxel was associated with a similarly higher response rate in both patients with ER(+) (odds ratio, 2.90; 95% confidence interval [CI], 1.72-4.87) and patients with ER(-) (odds ratio, 2.55; 95% CI, 1.44-4.51) disease. The lower hazard for disease progression with docetaxel was also similar in ER(+) (HR, 0.82; 95% CI, 0.67-1.00) and ER(-) (HR, 0.86; 95% CI, 0.70-1.07) cancers. The effect of docetaxel was not different in ER(+) and ER(-) disease, in terms of both the response rate and PFS time (interaction test, p = .77 and p = .93). INTERPRETATION: Docetaxel produces a higher response rate and lower risk for disease progression to a statistically similar extent in both patients with ER(+) and patients with ER(-) metastatic breast cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.024 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".