The incremental benefit of EUS for identifying unresectable disease among adults with pancreatic adenocarcinoma: A meta-analysis
Bibliographic record
Abstract
BACKGROUND AND STUDY AIMS: It is unclear to what extent EUS influences the surgical management of patients with pancreatic adenocarcinoma. This systematic review sought to determine if EUS evaluation improves the identification of unresectable disease among adults with pancreatic adenocarcinoma. PATIENTS AND METHODS: We searched MEDLINE, EMBASE, bibliographies of included articles and conference proceedings for studies reporting original data regarding surgical management and/or survival among patients with pancreatic adenocarcinoma, from inception to January 7th 2017. Our main outcome was the incremental benefit of EUS for the identification of unresectable disease (IBEUS). The pooled IBEUS were calculated using random effects models. Heterogeneity was explored using stratified meta-analysis and meta-regression. RESULTS: Among 4,903 citations identified, we included 8 cohort studies (study periods from 1992 to 2007) that examined the identification of unresectable disease (n = 795). Random effects meta-analysis suggested that EUS alone identified unresectable disease in 19% of patients (95% confidence interval [CI], 10-33%). Among those studies that considered portal or mesenteric vein invasion as potentially resectable, EUS alone was able to identify unresectable disease in 14% of patients (95% CI 8-24%) after a CT scan was performed. LIMITATIONS: The majority of the included studies were retrospective. CONCLUSIONS: EUS evaluation is associated with increased identification of unresectable disease among adults with pancreatic adenocarcinoma.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.062 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".