Pancreatic Enzyme Supplements Are Not Effective for Relieving Abdominal Pain in Patients with Chronic Pancreatitis: Meta-Analysis and Systematic Review of Randomized Controlled Trials
Bibliographic record
Abstract
Background. Pancreatic enzyme supplementation is widely used to treat pain in patients with chronic pancreatitis, despite little evidence for efficacy. We performed a systematic review of the literature and a meta-analysis to investigate its effectiveness. Methods. All randomized controlled parallel or crossover trials in patients with chronic pancreatitis comparing pancreatic enzyme supplementation to placebo were included. The main outcome was improvement in pain score or reduced analgesic consumption. Two independent reviewers extracted data. Mantel-Haenszel random effect model meta-analysis was used whenever methodologically appropriate. Results. Five out of 434 retrieved studies were included in the systematic review. All studies used relatively similar methodology. Four studies using enteric-coated pancreatic enzyme supplementation failed to show any improvement in pain as compared to placebo. The only study using non-enteric-coated enzymes did show reduction in the pain score. There was significant heterogeneity among studies in both analyses. Random model meta-analysis of three studies showed no significant difference in the mean of daily pain score (mean difference: 0.09 (1.57-1.39), p = 0.91) or average weekly analgesic consumption (mean difference: -0.30 (-2.37-1.77), p = 0.77) between the periods of administering pancreatic enzyme supplementation versus placebo. Conclusion. Pancreatic enzyme supplements do not seem to relieve abdominal pain in patients with chronic pancreatitis and should not be prescribed solely for this purpose, given their significant cost and potential side effects.
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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 | 0.023 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.042 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".