Gallstones and common bile duct stones management: Single-stage <i>vs</i> two-stage treatment
Bibliographic record
Abstract
BACKGROUND The optimal management of gallstones and common bile duct stones remains a subject of ongoing debate. The conventional two-stage treatment involves initial endoscopic retrograde cholangiopancreatography (ERCP) to clear the bile duct, followed by laparoscopic cholecystectomy. Alternatively, the single-stage laparoendoscopic rendezvous (LERV) procedure combines ERCP and laparoscopic cholecystectomy in the same surgical session. AIM To evaluate the efficacy, safety, and logistical considerations of these two approaches, emphasizing their implications for different healthcare settings. METHODS A literature search was conducted through a PubMed search (2010-2024) using the terms “laparoendoscopic rendezvous”, “endoscopic retrograde cholangiopancreatography”, and “cholecystocholedocholithiasis”. Only English-language studies were included. RESULTS In our analysis, LERV significantly reduced the incidence of post-ERCP pancreatitis by 67% (2.4% vs 8.8%) and shortened hospital stay by a mean of up to 6 days. Stone clearance rates were comparable between LERV (97%) and the two-stage approach (96%). Although LERV was associated with a longer operative time (139.8 minutes vs 107.7 minutes), it demonstrated lower overall costs, largely due to reduced hospitalization. Rates of postoperative bleeding, cholangitis, and bile leak were low and did not differ significantly between groups. CONCLUSION The single-stage LERV approach is safe, effective, and associated with lower pancreatitis rates, shorter hospital stays, and reduced costs compared to the two-stage strategy. Its implementation, however, requires coordinated surgical-endoscopic expertise, making it most suitable for well-equipped centers and carefully selected patients.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".