Novel therapeutic strategies for advanced pancreatic cancer: targeting the epidermal growth factor and vascular endothelial growth factor pathways
Bibliographic record
Abstract
The introduction of novel agents targeted to specifi c molecular targets of cancer cells offers more treatment options and improvement in outcome for exocrine pancreatic adenocarcinoma. Due to the limitations in the scope and scale of researches, this review of clinical studies presents the effects of administering the agents targeting the receptors or ligands of epidermal growth factor (EGF) or vascular endothelial growth factor (VEGF) to treat advanced pancreatic carcinoma. In addition, the basic knowledge on the mechanisms of therapy targeting EGFR and VEGFR were described. Among all agents, erlotinib has been approved by the U.S. Food and Drug Administration and incorporated in a number of treatment guidelines. It has been shown, in a randomized phase III clinical trial reported by the National Cancer Institute of Canada, to extend survival when used in combination with gemcitabine; however, its use as monotherapy has not produced significant efficacy. Furthermore, results from a multicenter randomized clinical trial has demonstrated that the combined utilization of erlotinib and bevacizumab, in current with gemcitabine signifi cantly improved the progression-free survival, but has no signifi cant effect on overall survival of patients with advanced pancreatic cancer. Other agents including cetuximab, bevacizumab, sorafenib, sunitinib, etc. used along or in combination with chemotherapy are under active investigation..
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".