Bibliographic record
Abstract
10.1055/a-2308-3777 We would like to thank Saito T and colleagues for their comments about and interest in our study about the timing of lumen-apposing metal stent (LAMS) removal during endoscopic ultrasound-guided treatment of pancreatic fluid collections (PFCs) [ 1 ]. We agree with the authors that a better understanding of which PFCs will require longer LAMS placement is needed to optimize patient care. Here are some details about our results. The major reasons for clinical failure in the early stent removal group were either exacerbating infection despite endoscopic management or recurrent sepsis after stent removal. We also experienced adverse events (AEs) such as stent dislodgement during early necrosectomies, which resulted in clinical failure. Walled-off necrosis (WON) was associated with a lower clinical success rate in both the early stent removal group (61.5%) and the delayed stent removal group (94.6%) as compared with the pseudocyst group (85.7% and 100% respectively). As therapeutic endoscopists who treat PFCs on a regular basis, many of us have experienced the difference between a simple collection that can be drained in one session and more complex, larger, debris-filled collections which will likely require multiple interventions [ 2 ]. In our experience, patience is key in management of this second group of patients. The inflammatory process following the initial insult in acute pancreatitis can take several weeks to resolve [ 3 ]. We believe a more conservative approach, with longer stent placement for passive drainage, can reduce the need for necrosectomies or stent replacement, both of which can cause AEs and result in clinical failure [ 4 ] [ 5 ]. Finally, we agree with our colleagues: A large prospective clinical trial is now needed to better understand which patients will benefit from longer LAMS placement. Before we move forward, the endoscopic ultrasound community needs to standardize the definition of treatment success and refine the classification of WON to better characterize large and complex collections that will likely require multiple interventions. Publication History Received: 25 March 2024 Accepted: 09 April 2024 Article published online: 21 May 2024 © 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/). Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.007 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.039 | 0.050 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".