Preoperative Endoscopic Ultrasound Fine Needle Aspiration Versus Upfront Surgery in Resectable Pancreatic Cancer: A Systematic Review and Meta-analysis of Clinical Outcomes Including Survival and Risk of Tumor Recurrence
Bibliographic record
Abstract
Background and Aim: Endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) is the standard of care in advanced pancreatic cancer. Its role in resectable disease, however, is controversial. This meta-analysis aims to ascertain the clinical outcomes of patients with resectable pancreatic cancer undergoing preoperative EUS-FNA compared to those going directly to surgery. Methods: A literature search was performed from 1996 to April 2019 using MEDLINE, EMBASE, and ISI Web of Knowledge for studies comparing preoperative EUS-FNA to EUS without FNA in resectable pancreatic cancer for clinical outcomes. The primary outcome is overall survival (OS). Secondary outcomes include cancer-free survival, tumor recurrence and peritoneal carcinomatosis, and post-FNA-pancreatitis rate. Results: Six retrospective studies were included. Preoperative EUS-FNA had better OS than the non-FNA group (WMD, 4.40 months [0.02 to 8.78]). Cancer-free survival did not differ significantly between the two groups (WMD, 2.08 months [-2.22 to 6.38]). EUS with FNA was not associated with increased rates of tumor recurrence or peritoneal carcinomatosis. Conclusion: Preoperative EUS-FNA in resectable pancreatic cancer may be associated with significantly greater OS when compared to the non-FNA group, with no significant difference in the rates of tumor recurrence or peritoneal seeding. Important limitations of our meta-analysis include the lack of prospective controlled data, which are unlikely to emerge given feasible constraints.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".