Erlotinib in the treatment of advanced pancreatic cancer
Bibliographic record
Abstract
Single agent gemcitabine has been the mainstay of therapy for advanced pancreatic cancer over the past decade. Multiple trials of newer chemotherapeutic agents both alone and in combination have yielded disappointing results, spurring the ongoing search for new agents and combinations in this aggressive malignancy. Inhibitors of the epidermal growth factor receptor (EGFR) have shown promising activity in multiple solid tumors types, and preclinical data support a role for EGFR inhibition in pancreatic cancer. A recent phase III study by the National Cancer Institute of Canada Clinical Trials Group (NCIC-CTG) demonstrated a significant survival benefit with the addition of the EGFR tyrosine kinase inhibitor, erlotinib, to gemcitabine chemotherapy for the first-line treatment of patients with advanced pancreatic cancer, becoming the first phase III study to demonstrate a survival benefit of combination therapy as well as targeted therapy in this disease. This article reviews the evidence supporting EGFR inhibition and the use of erlotinib in advanced pancreatic cancer as well as future implications of targeted therapy in this challenging malignancy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".