A234 LUMEN APPOSING METAL STENTS VERSUS PLASTIC STENTS IN THE MANAGEMENT OF PANCREATIC PSEUDOCYST: A COST-EFFECTIVENESS ANALYSIS
Bibliographic record
Abstract
EUS-guided drainage is an effective and accepted primary modality for the management of 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). Comparing the cost-effectiveness of LAMS to PS. A decision tree is developed assessing both endoscopic drainage strategies for patients with PP: LAMS and PS over a 6 months time horizon. For each strategy, in-patients received a stent and are followed for subsequent need for direct further interventions or adverse events leading to unplanned endoscopy, percutaneous drainage (PCD), surgery, or successful endoscopic drainage using probabilities obtained from the literature. The unit of effectiveness is successful endoscopic drainage without need for PCD or surgery. Costs in 2016 US$ are based on inpatient institutional costs. Physician fees are obtained from the American Medical Association. A third-party payer perspective is adopted and sensitivity analyses performed. The success rate is 93.9% for LAMS and 96.96% for PS. Respective costs per successful drainage are US$18,129 (LAMS) and US$10,403 (PS). Being both more costly and less effective, the LAMS strategy is thus characterized as dominated (in the economic sense) by the PS approach. Both deterministic and probabilistic sensitivity analyses confirm the robustness of these findings. . The use of LAMS is not more effective and more costly than PS in the management of patients with PP. As such, PS should be preferred over LAMS as initial management of these patients. None
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".