Use of endoscopic ultrasound-guided gallbladder drainage as a rescue approach in cases of unsuccessful biliary drainage
Bibliographic record
Abstract
This narrative review provides an overview of the utilization of endoscopic ultrasound-guided gallbladder drainage (EUS-GBD) as a salvage approach in cases of unsuccessful conventional management. EUS-GBD is a minimally invasive and effective technique for drainage in patients with acute cholecystitis with high risk of surgery. The procedure has demonstrated impressive technical and clinical success rates with low rates of adverse events, making it a safe and effective option for appropriate candidates. Furthermore, EUS-GBD can also serve as a rescue option for patients who have failed endoscopic retrograde cholangiopancreatography or EUS biliary drainage for relief of jaundice in malignant biliary stricture. However, patient selection is critical for the success of EUS-GBD, and proper patient selection and risk assessment are important to ensure the safety and efficacy of the procedure. As the field continues to evolve and mature, ongoing research will further refine our understanding of the benefits and limitations of EUS-GBD, ultimately leading to improved outcomes for patients.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".