Pancreatic Enzyme Use Reduces Pancreatitis Frequency in Children With Acute Recurrent or Chronic Pancreatitis: A Report From INSPPIRE
Bibliographic record
Abstract
INTRODUCTION: Among children who suffer from acute recurrent pancreatitis (ARP) or chronic pancreatitis (CP), acute pancreatitis (AP) episodes are painful, often require hospitalization, and contribute to disease complications and progression. Despite this recognition, there are currently no interventions to prevent AP episodes. In this retrospective cohort study, we assessed the impact of pancreatic enzyme therapy (PERT) use on clinical outcomes among children with pancreatic-sufficient ARP or CP. METHODS: Children with pancreatic-sufficient ARP or CP in the INSPPIRE-2 cohort were included. Clinical outcomes were compared for those receiving vs not receiving PERT, as well as frequency of AP before and after PERT. Logistic regression was used to study the association between development of AP episodes after starting PERT and response predictors. RESULTS: Among 356 pancreatic-sufficient participants, 270 (76%) had ARP, and 60 (17%) received PERT. Among those on PERT, 42% did not have a subsequent AP episode, during a mean 2.1 years of follow-up. Children with a SPINK1 mutation ( P = 0.005) and those with ARP (compared with CP, P = 0.008) were less likely to have an AP episode after starting PERT. After initiation of PERT, the mean AP annual incidence rate decreased from 3.14 down to 0.71 ( P < 0.001). DISCUSSION: In a retrospective analysis, use of PERT was associated with a reduction in the incidence rate of AP among children with pancreatic-sufficient ARP or CP. These results support the need for a clinical trial to evaluate the efficacy of PERT to improve clinical outcomes among children with ARP or CP.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".