ECF chemotherapy for liver metastases due to castration-resistant prostate cancer
Bibliographic record
Abstract
INTRODUCTION: Most men with metastatic castration-resistant prostate cancer (CRPC) have biochemical response to docetaxel, but the objective response rate is low. Liver metastases are uncommon with CRPC and associated with shorter survival. More active treatment might benefit these patients. Epirubicin, cisplatin and flurouracil (ECF) is a standard regimen for gastric cancer and response in CRPC liver metastases has been reported. We reviewed our experience with ECF in CRPC with the primary objective of determining its anti-tumour activity in patients with liver metastatic CRPC. METHODS: Men with CRPC treated with ECF were identified from electronic databases and data were extracted from medical records. Men with tumours showing neuroendocrine features were excluded. RESULTS: In total, we identified 14 CRPC patients treated with ECF were identified, of which 8 had liver metastases. The median age was 56 (range: 42-76) and all had multiple poor prognostic features. A median of 6 cycles of ECF were administered (range: 1-10) and toxicities were similar to previous reports. Of the 8 patients with liver metastases, 5 had partial remission. CONCLUSIONS: ECF was highly active in this small selected group of younger men with liver metastases from CRPC and multiple poor prognostic features. Despite important limitations, this is the third report of high objective response rates with ECF in CRPC. Objective response rates are low with current monotherapies. A higher probability of ORR is preferred for critical organ disease, therefore the anti-tumour activity should encourage testing of ECF in comparison to the most active current therapies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".