Plastic stents are more cost-effective than lumen-apposing metal stents in management of pancreatic pseudocysts
Bibliographic record
Abstract
BACKGROUND AND STUDY AIMS: Endoscopic ultrasound-guided drainage is an effective and accepted primary modality for management of pancreatic pseudocyst (PP). A lumen-apposing metal stent (LAMS) has recently been developed specifically for drainage of pancreatic fluid collections which may be superior to using traditional plastic stents (PS) but is more expensive. Because use of a stent involves a risk of unplanned endoscopy, percutaneous drainage (PCD) and surgery, their costs should also be included in the comparison and a cost-effectiveness analysis of LAMS and PS should therefore be performed. PATIENTS AND METHODS: A decision tree was developed assessing both endoscopic drainage strategies for patients with PP: LAMS and PS over a 6-month time horizon. For each strategy, inpatients received a stent and were followed for subsequent need for direct further interventions or adverse events leading to unplanned endoscopy, PCD, surgery, or successful endoscopic drainage using probabilities obtained from the literature. The unit of effectiveness was successful endoscopic drainage without need for PCD or surgery. Sensitivity analyses were performed. RESULTS: Success rates were 93.9 % for LAMS and 96.96 % for PS. Respective costs per successful drainage were US $ 18,129 (LAMS) and US $ 10,403 (PS). The LAMS strategy was thus characterized as dominated by the PS approach because it was costlier and less effective than PS. Both deterministic and probabilistic sensitivity analyses confirmed the robustness of these findings. CONCLUSION: Use of LAMS is not less effective and more costly than PS in management of patients with PP. As such, PS should be preferred over LAMS as initial management of these patients.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".