Dose escalation to rash for erlotinib plus gemcitabine for metastatic pancreatic cancer: the phase II RACHEL study
Bibliographic record
Abstract
BACKGROUND: This phase II, open-label, randomised study evaluated whether patients with metastatic pancreatic cancer receiving erlotinib/gemcitabine derived survival benefits from increasing the erlotinib dose. METHODS: After a 4-week run-in period (gemcitabine 1000 mg m(-2) once weekly plus erlotinib 100 mg per day), patients with metastatic pancreatic cancer who developed grade 0/1 rash were randomised to receive gemcitabine plus erlotinib dose escalation (150 mg, increasing by 50 mg every 2 weeks (maximum 250 mg); n=71) or gemcitabine plus standard-dose erlotinib (100 mg per day; n=75). The primary end point was to determine whether overall survival (OS) was improved by increasing the erlotinib dose. Secondary end points included progression-free survival (PFS), incidence of grade ⩾2 rash, and safety. RESULTS: Erlotinib dose escalation induced grade ⩾2 rash in 29 out of 71 (41.4%) patients compared with 7 out of 75 (9.3%) patients on standard dose. Efficacy was not significantly different in the dose-escalation arm compared with the standard-dose arm (OS: median 7.0 vs 8.4 months, respectively, hazard ratio (HR), 1.26, 95% confidence interval (CI): 0.88-1.80; P=0.2026; PFS: median 3.5 vs 4.5 months, respectively, HR, 1.09, 95% CI: 0.77-1.54; P=0.6298). Incidence of adverse events was comparable between randomised arms. CONCLUSION: The erlotinib dose-escalation strategy induced rash in some patients; there was no evidence that the higher dose translated into increased benefit.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".