Efficacy of spyglass-guided electrohydraulic lithotripsy in difficult bile duct stones
Bibliographic record
Abstract
BACKGROUND/AIMS: We aimed to evaluate the efficacy and safety of Spyglass-guided electrohydraulic lithotripsy (EHL) for difficult common bile duct stones (CBD) not amenable to conventional endoscopic therapy. DESIGN: A retrospective study evaluating the efficacy of Spyglass-guided EHL in treating difficult CBD stones, in a single tertiary care center. PATIENTS AND METHODS: All patients who underwent Spyglass-guided EHL from 2012 to 2013 were compared with a historical cohort who had ECSWL. RESULTS: A total number of 13 patients underwent Spyglass-guided EHL, 8 (61.5%) of them were males. The mean age was 46.5 ± 5.6 years. Bile duct clearance was achieved in 13 (100%) of them. Seventy-six percent required only one Endoscopic Retrograde Cholangiopancreatography (ERCP) to clear the CBD, 7.7% required two ERCPs, and 15.4% required three ERCPs. Adverse effects (cholangitis) occurred in one patient (10%), whereas only 30 patients (64.4%) of the ESWL group had complete CBD stone clearance. Thirty-seven percent required one ERCP to clear the CBD, 35.6% required two ERCPs, and 20% required three ERCPs. Adverse effects happened in seven (15.5%) patients, where five (11%) had cholangitis and two (4.4%) had pancreatitis. CONCLUSION: Although a retrospective design with a small sample size, we concluded that Spyglass-guided EHL is an effective procedure in treating difficult CBD stones.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".