Evolution of systemic therapy for advanced pancreatic cancer
Bibliographic record
Abstract
The prognosis for advanced pancreatic cancer remains poor and successful drug development in this disease continues to be a major challenge. In the last decade the approach to drug development in pancreatic cancer has included a focus on combinations of cytotoxic agents. While some promising results were seen in Phase II studies, none of the Phase III trials of cytotoxic combinations were able to demonstrate an improvement in overall survival over that seen with the single-agent gemcitabine. Newer studies have assessed the efficacy of 'targeted' agents that inhibit pathways thought to be important in the development, growth, invasion and metastasis of pancreatic cancer. Although some agents had promising activity in preclinical studies, none has made a major impact in the clinic. There has been some success with the addition of the EGF receptor tyrosine kinase inhibitor erlotinib to gemcitabine, which was the first combination to achieve an overall survival benefit compared with gemcitabine alone in a Phase III trial. Future directions for drug development in pancreatic cancer will mainly involve testing new targeted agents, although some cytotoxic combinations are currently in Phase III testing. There is a need to better understand the biology of the disease and incorporate this into trials in an attempt to search for predictive and prognostic markers that will aid in drug development. Control of pancreatic cancer will require combinations of targeted agents, probably individualized based on tumor genetics. We are just beginning to explore the efficacy of combining targeted agents in the clinic.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".