Endoscopic and Surgical Treatments for Painful Chronic Pancreatitis: A Scoping Review of Pain Assessment Tools and Meta-analysis of Outcomes
Bibliographic record
Abstract
OBJECTIVE: Managing painful chronic pancreatitis (CP) often involves invasive treatments, but success rates are variable. We aimed to describe the pain assessment tools used to measure the efficacy of endotherapy and surgery for painful CP and perform a meta-analysis of outcomes. DESIGN: PubMed, Embase, and Scopus databases were searched for published studies through April 1, 2023. Full papers in English that assessed pain outcomes among adults with painful CP undergoing invasive interventions were included. RESULTS: There were 413 out of 1,282 studies that underwent full-text review, and 279 studies were selected for the scoping review. Most commonly used pain assessment tools included symptom description (n=68 studies), numeric pain rating scales (NRS) or visual analog scales (VAS) (n=52), binary pain relief (yes or no) (n=27), and the pancreatitis-specific 4-item Izbicki score (n=28). In a meta-analysis of studies reporting preintervention and postintervention NRS or VAS (0-100), the mean decrease in pain after endoscopic intervention (n=9 studies) was 40.3 (95% CI: 27-53.6, P <0.001) and after surgical intervention (n=12 studies) it was 43.2 (95% CI: 31.5-54.9, P <0.001). A separate meta-analysis of studies reporting the preintervention and postintervention Izbicki score (n=5) showed similar findings. There was no difference in the change in pain scores between endotherapy and surgical cohorts in studies using NRS/VAS or Izbicki scores. CONCLUSIONS: Pain outcomes were similar between endotherapy and surgery for painful CP based on the use of simple and highly variable pain assessment tools. Referral bias and sham effects need to be considered in future trials.
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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 | 0.037 | 0.092 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.044 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".