Endoscopic Ultrasound Guided Fine Needle Aspiration versus Endoscopic Ultrasound Guided Fine Needle Biopsy for Pancreatic Cancer Diagnosis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: One of the most effective diagnostic tools for pancreatic cancer is endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) or biopsy (EUS-FNB). Several randomized clinical trials have compared different EUS tissue sampling needles for the diagnosis of pancreatic cancer. OBJECTIVE: To compare the diagnostic accuracy of EUS-guided FNA as EUS-FNB needles for the diagnosis of pancreatic cancer using a systematic review and meta-analysis. METHOD: A literature review with a meta-analysis was performed according to the PRISMA guide. The databases of PubMed, Cochrane and Google Scholar were used, including studies published between 2011-2021 comparing the diagnostic yield (diagnostic accuracy or probability of positivity, sensitivity, specificity, predictive value) of EUS-FNA and EUS-FNB for the diagnosis of pancreatic cancer. The primary outcome was diagnostic accuracy. Random effect models allowed estimation of the pooled odds ratio with a confidence interval (CI) of 95%. RESULTS: Nine randomized control trials were selected out of 5802 articles identified. Among these, five studies found no statistically significant difference between the EUS-FNA and EUS-FNB, whereas the other four did. The meta-analysis found EUS-FNB accuracy superior to EUS-FNA for the diagnosis of pancreatic cancer with a pooled odds ratio of 1.87 (IC 95%: 1.33-2.63). CONCLUSION: As compared to EUS-FNA, EUS-FNB seems to improve diagnostic accuracy when applied to suspicious pancreatic lesions.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".