Gemcitabine as First-Line Therapy in Patients with Metastatic Breast Cancer: A Phase II Trial
Bibliographic record
Abstract
OBJECTIVES: This phase II study was conducted to evaluate the efficacy and safety of gemcitabine in patients with metastatic breast cancer (MBC). METHODS: Women with histologically or cytologically confirmed bidimensionally measurable MBC not amendable to curative surgery or radiation were eligible. Prior chemotherapy for metastatic disease was not permitted. Patients received gemcitabine 1,200 mg/m(2) on days 1, 8 and 15 for 3 weeks every 28 days for a maximum of 8 cycles. RESULTS: Thirty-nine patients, with a median age of 58 years, were enrolled. The overall response rate for the 35 evaluable patients was 37.1% (95% confidence interval [CI], 21.5-55.1%), with 2 complete responses and 11 partial responses. Median time to progression and survival were 5.1 months (95% CI, 3.5-8.8 months) and 21.1 months (95% CI, 11.0-26.9 months), respectively. Chemotherapy was well tolerated, with a median of 4 cycles completed. Grade 4 toxicities were 1 infection and 1 abnormal pulmonary function. Grade 3 neutropenia and thrombocytopenia occurred in 30.3% and 6.3% of patients, respectively. The most common grade 3 non-hematologic toxicity was nausea/vomiting (10.3%). Five of 21 patients had improved Karnofsky performance status (KPS) scores. CONCLUSION: Single-agent gemcitabine is active and well tolerated as first-line treatment in patients with MBC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".