Single‐operator peroral cholangioscope in treating difficult biliary stones: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND AIM: Current evidence supporting the utility of single-operator peroral cholangioscope (SOPOC) in the management of difficult bile duct stones is limited. We conducted the present systematic review and meta-analysis to evaluate the efficacy and safety of SOPOC in treating difficult bile duct stones. METHODS: We searched studies up to April 2018, using MEDLINE, EMBASE, the Cochrane Library, and Google Scholar. Quality assessment of the studies was completed with the Newcastle-Ottawa Scale. Main outcomes were complete stone clearance rate, single-session stone clearance rate, number of endoscopic sessions needed for stone clearance, and adverse events. We calculated the pooled estimates with random-effects models. Potential publication bias was assessed. RESULTS: Twenty-four studies involving 2786 patients met the inclusion criteria. Pooled proportion of patients with complete stone clearance was 94.3% (95% confidence interval [95% CI]: 90.2-97.5%). Single-session stone clearance was achieved in 71.1% (95% CI: 62.1-79.5%) of the pooled patients. Pooled number of sessions needed for stone clearance was 1.26 (95% CI: 1.17-1.34%). Pooled adverse event rate was 6.1% (95% CI: 3.8-8.7%). Potential publication bias was detected but had no significant influence on the results. CONCLUSIONS: Single-operator peroral cholangioscope is an effective and safe treatment for difficult bile duct stones when conventional methods have failed. More randomized controlled trials are warranted to confirm the results.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".