OC-073 A comparison of outcomes between a single device lumen-apposing metal stent with electro cautery-enhanced delivery system and a bi-flanged multi-step system metal stent for drainage of walled off pancreatic necrosis
Bibliographic record
Abstract
Introduction Recently, purpose designed stents have become available for EUS guided cystgastrostomy and drainage of walled of pancreatic necrosis (WON). The first such stent is a bi-flanged metal stents (BFMS). Bi-flanged metal stents (BFMS) have shown promise in the drainage of walled-off pancreatic necrosis (WON) but require multiple steps and use of other devices for placement. More recently, a novel device consisting of a combined lumen-apposing metals stent (LAMS) and electrocautery-enhanced delivery system has been introduced. This enables a single device to be used when previously multiple devices and steps were required with potential time saving and reduced immediate adverse events.The LAMS is considerably more expensive than the BFMS stent. Studies comparing BFMS to the new generation single step LAMS are lacking. Aims To compare procedure time, technical and clinical success, costs and composite endpoint of significant events (adverse events, stent migration, additional percutaneous drainage) between BFMS and LAMS. Method Retrospective review of consecutive BFMS/LAMS cases between October 2012 and December 2016, in a prospectively maintained database, undergoing EUS guided drainage of symptomatic WON. Results A total of 77 patients underwent consecutive BFMS (44) and LAMS (33) placement. Successful placement was achieved in 91% BFMS and 97% LAMS. Median [range] in-room procedure time was significantly shorter (45 [30-80] minutes vs 62.5 [35-135], p<0.0001) and fewer (DEN) were performed (median 1 [0–8] vs 2 [0–11], p=0.005) in the LAMS group. Excluding ambulatory care patients, comparable direct endoscopic necrosectomies (DEN) procedures were found between 35 BFMS 2 [0–11] and 19 LAMS 2 [0–8] patients. Composite endpoint 30% vs 21% and clinical success 65% vs 78% were comparable. Mean [95% CI] procedural costs for BFMS were £4551 [3738–5363] ($5669) versus £4263 [3182–5344] ($5310) for LAMS; p=ns Conclusion The LAMS was superior to the BFMS in terms of procedure time with comparable adverse events, success and costs. Disclosure of Interest None Declared
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".