A meta-analysis of clinical benefit rates for fulvestrant 500 mg vs. alternative endocrine therapies for hormone receptor-positive advanced breast cancer
Bibliographic record
Abstract
BACKGROUND: Fulvestrant, a selective estrogen receptor degrader, is approved for first- and second-line treatment of postmenopausal women with hormone receptor-positive advanced breast cancer (ABC). METHODS: Meta-analysis of randomized controlled trials (RCTs) evaluating fulvestrant 500 mg in postmenopausal hormone receptor-positive ABC, to evaluate differences in clinical benefit rate (CBR; proportion of patients experiencing best overall response of complete response, partial response, or stable disease for ≥ 24 weeks) between fulvestrant 500 mg and comparator endocrine therapies. Odds ratios (OR) and 95% confidence intervals (CI) for CBR were calculated; fixed effects (FE) models were constructed (first- and second-line data, alone and combined). RESULTS: Six RCTs were included. Four studies evaluated fulvestrant 500 mg vs. fulvestrant 250 mg; two evaluated fulvestrant 500 mg vs. anastrozole 1 mg. In total, 1054 and 534 patients were included (first- and second-line treatment, respectively). Analysis of OR and 95% CI of CBR by therapy line favored fulvestrant 500 mg vs. comparator therapy. Assessing all results combined in the FE model indicated significant improvement in CBR with fulvestrant 500 mg vs. comparator treatments (OR 1.33; 95% CI 1.13-1.57; p = 0.001). Restricting the FE model to therapy line demonstrated significant improvement in CBR vs. comparator treatments (OR 1.33; 95% CI 1.02-1.73; p = 0.035) for first-line, and a trend to improvement vs. comparator treatments (OR 1.27; 95% CI 0.90-1.79; p = 0.174) for second-line. CONCLUSIONS: In postmenopausal patients with hormone receptor-positive ABC, fulvestrant 500 mg first-line was associated with significantly greater CBR (more patients benefiting from treatment) vs. comparator endocrine therapy.
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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 | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.083 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".