Recent developments in palliative chemotherapy for locally advanced and metastatic pancreas cancer
Bibliographic record
Abstract
In spite of advances made in the management of the other more common cancers of the gastrointestinal tract, significant progress in the treatment of pancreatic cancer remains elusive. Nearly as many deaths occur from pancreatic cancer as are diagnosed each year reflecting the poor prognosis typically associated with this disease. Until recently, the only treatment with an impact on survival was surgery. In the palliative setting, gemcitabine (Gem) has been a standard treatment for advanced pancreatic cancer since it was shown a decade ago to result in a superior clinical benefit response and survival compared with bolus 5-fluorouracil. Since then, clinical trials have explored the pharmacokinetic modulation of Gem by fixed dose administration and the combination of Gem with other cytotoxic or the biologically "targeted" agents. However, promising trial results in small phase II trials have not translated into survival improvements in larger phase III randomized trials in the advanced disease setting. Two trials have recently reported modest survival improvements with the use of combination treatment with Gem and capecitabine (United Kingdom National Cancer Research GEMCAP trial) or erlotinib (National Cancer Institute of Canada Clinical Trials Group PA.3 trial). This review will focus on the use of systemic therapy for advanced and metastatic pancreatic cancer, summarizing the results of several recent clinical trials and discuss their implications for clinical practice. We will also discuss briefly the second-line chemotherapy options for advanced pancreatic cancer.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".