Lumen apposing metal stents are superior to plastic stents in pancreatic walled-off necrosis: a large international multicenter study
Bibliographic record
Abstract
Abstract Background and study aims The use of lumen apposing metal stents (LAMS) during EUS-guided transmural drainage (EUS-TD) of pancreatic walled-off necrosis (WON) has gained popularity. Data supporting their use in WON over plastic stents (PS), however, remain scarce. The aim of this study was to compare the clinical efficacy of LAMS (Axios, Boston Scientific) with PS in WON. Patients and methods This was a multicenter, retrospective study involving 14 centers. Consecutive patients who underwent EUS-TD of WON (2012 – 2016) were included. The primary end point was clinical success defined as WON size ≤ 3 cm within a 6-month period without need for percutaneous drainage (PCD) or surgery. Results A total of 189 patients (mean age 55.2 ± 15.6 years, 34.9 % female) were included (102 LAMS and 87 PS). Technical success rates were similar: 100 % in LAMS and 98.9 % in PS (P = 0.28). Clinical success was attained in 80.4 % of LAMS and 57.5 % of PS (P = 0.001). Rate of PCD was similar (13.7 % LAMS vs. 16.3 % PS, P = 0.62), while PS was associated with a greater need for surgery (16.1 % PS vs. 5.6 % LAMS, P = 0.02). Adverse events (AEs) were observed in 9.8 % of LAMS and 10.3 % of PS (P = 0.90) and were rated as severe in 2.0 % and 6.9 %, respectively (P = 0.93). After excluding patients with < 6 months follow-up, the rate of WON recurrence following initial clinical success was greater with PS (22.9 % PS vs. 5.6 % LAMS, P = 0.04). Conclusions When compared to PS, LAMS in WON is associated with higher clinical success, shorter procedure time, lower need for surgery, and lower rate of recurrence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".