Technical Factors Associated With the Benefit of Prophylactic Pancreatic Stent Placement During High-Risk Endoscopic Retrograde Cholangiopancreatography: A Secondary Analysis of the SVI Trial Data Set
Bibliographic record
Abstract
INTRODUCTION: Prophylactic pancreatic stent placement (PSP) is effective for preventing pancreatitis after endoscopic retrograde cholangiopancreatography (ERCP) in high-risk cases, but the optimal technical approach to this intervention remains uncertain. METHODS: In this secondary analysis of 787 clinical trial patients who underwent successful stent placement, we studied the impact of (i) whether pancreatic wire access was achieved for the sole purpose of PSP or naturally during the conduct of the case, (ii) the amount of effort expended on PSP, (iii) stent length, (iv) stent diameter, and (v) guidewire caliber. We used logistic regression models to examine the adjusted association between each technical factor and post-ERCP pancreatitis (PEP). RESULTS: Ninety-one of the 787 patients experienced PEP. There was no clear association between PEP and whether pancreatic wire access was achieved for the sole purpose of PSP (vs occurring naturally; odds ratio [OR] 0.82, 95% confidence interval [CI] 0.37-1.84), whether substantial effort expended on stent placement (vs nonsubstantial effort; OR 1.58, 95% CI 0.73-3.45), stent length (>5 vs ≤5 cm; OR 1.01, 95% CI 0.63-1.61), stent diameter (≥5 vs <5 Fr; OR 1.13, 95% CI 0.65-1.96), or guidewire caliber (0.035 vs 0.025 in; 0.83, 95% CI 0.49-1.41). DISCUSSION: The 5 modifiable technical factors studied in this secondary analysis of large-scale randomized trial data did not appear to have a strong impact on the benefit of prophylactic PSP in preventing PEP after high-risk ERCP. Within the limitations of post hoc subgroup analysis, these findings may have important implications in procedural decision making and suggest that the benefit of PSP is robust to variations in technical approach.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".