Performance Characteristics of a Lumen-Apposing Metal Stent for Pancreatic Fluid Collections: A Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Endoscopic ultrasound-guided transmural drainage is the preferred management of pancreatic fluid collections (PFCs). Optimizing drainage is important and there remains debate as to the choice of stent. A recent trend towards the use of lumen-apposing metal stents (LAMS) has emerged. AIM: To evaluate the performance characteristics of a LAMS based on a prospective protocol (CT scan 1 week after placement to assess for resolution and need for necrosectomy followed by stent removal within 3 weeks). METHODS: This is a descriptive prospective cohort study performed at a single centre. The primary outcome was clinical success. Secondary outcomes were technical success, procedure time, total number of endoscopic procedures with or without necrosectomy, stent indwell time, stent functionality and adverse events. RESULTS: Thirty-seven patients (21 males, mean age 46.5 years) underwent placement of LAMS for 41 PFCs (median size 12 cm). There were 18 pseudocysts and 23 walled-off necrosis. Clinical success was seen in 33 of 41 (80%) PFCs. Of the remaining eight patients, six underwent surgery and two patients died from underlying malignant disease (although their PFC had completely resolved). Technical success and stent functionality were 100%. The median procedure time was 14 min (interquartile range 11 min to 20 min). Of the 23 walled-off necrosis, 9 (39%) required necrosectomy. The median stent indwell time was 19 days (interquartile range 14 to 22 days). There were no serious adverse events. CONCLUSIONS: Our protocol demonstrates excellent performance characteristics of LAMS. Their clinical efficacy and favourable safety profile suggest that they may be the preferred modality for endoscopic ultrasound-guided management of PFCs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".