A182 ENDOSCOPIC ULTRASOUND GUIDED PANCREATIC FLUID COLLECTIONS DRAINAGE USING A LUMEN-APPOSING METAL STENT WITH ELECTRO-CAUTERY ENHANCED DELIVERY SYSTEM (HOT AXIOS); INITIAL EXPERIENCE OF A TERTIARY CARE CENTER.
Bibliographic record
Abstract
Pancreatic fluid collections (PFC), including walled-off necrosis (WON), is a major complication of acute pancreatitis. It can pose significant morbidity to patients as well as a treatment challenge to physicians. Recently, endoscopic ultrasound (EUS) guided drainage using a novel lumen-apposing metal stent (LAMS) with electro-cautery enhanced delivery system (Hot AXIOS, Boston Scientific Corp., Marlborough, MA) has been used for draining these collections. We report our initial experience as a tertiary center in Canada. Tertiary center experience in managing pancreatic fluid collections using the novel lumen-apposing metal stent with electro-cautery enhanced delivery system. This is a retrospective chart review study of all the cases that underwent EUS guided PFC or WON drainage using the Hot AXIOS system in a tertiary medical center. Technical success is defined by successful deployment of the stent under EUS guidance. Clinical success was defined by symptom control with resolution or significant size reduction of the collection on follow up imaging or endoscopy. A total of 14 patients were included in the study. Mean age was 52.57 years and 10 (71.4%) were male. The mean size of the collections was 10.31cm. The most common causes of pancreatitis were alcohol (42.9%) and gallstone disease (21.4%). Mean procedure time was 19.7 minutes. Technical success was achieved in 100% of cases. Clinical success was achieved in 13 patients (92.7%). Adverse events were encountered in 2 patients (14.3%). One patient developed a mild peri-splenic hematoma which was managed conservatively, and another case was complicated by hemorrhage within the collection which was managed with radiological embolization. All stents were removed in 2 to 4 weeks after insertion Our initial experience in technical, clinical successes and adverse events with the novel hot AXIOS system is similar to what is reported in the literature. It is safe and effective, and the shorter procedure time is a major advantage over conventional methods. This is especially convenient for centers using conscious sedation for endoscopy 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".