Acute pancreatitis in pregnancy: a comparison of associated conditions, treatments and complications
Bibliographic record
Abstract
OBJECTIVE: Acute pancreatitis is a rare condition in pregnancy. The aim of this study is to compare associated conditions, treatments and complications of pancreatitis in pregnant and age-matched non-pregnant controls. METHODS: We carried out a population-based retrospective cohort study using the Healthcare Cost and Utilization Project Nationwide Inpatient Sample (HCUP-NIS) from 2003 to 2010. A cohort of pregnant women with acute pancreatitis was created and compared to a created age-matched cohort of non-pregnant women with acute pancreatitis at a 1:4 ratio. Comparisons of associated conditions, treatment types, and complications were carried out using unconditional logistic regression. RESULTS: We identified 7725 cases of acute pancreatitis in pregnancy. As compared to non-pregnant controls, pancreatitis in pregnancy was more likely to be associated with cholelithiasis and less likely with hyperlipidemia and alcohol abuse. Pancreatitis in pregnancy was more likely to be treated with parenteral nutrition and less likely to undergo endoscopic sphincterotomy. As compared to non-pregnant controls, pregnant women with pancreatitis were less likely to have pancreatic pseudocysts/hemorrhage/necrosis, generalized peritonitis, adult respiratory distress syndrome, disseminated intravascular coagulation, and death. CONCLUSION: Pancreatitis in pregnancy is predominantly caused by cholelithiasis, and unlike in the non-pregnant state, usually has a milder course.
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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".