Evaluating the Impact of Co-Existent Inflammatory Bowel Disease on Hospital-Based Outcomes Among Patients With Acute Pancreatitis: An Analysis of the 2020 National Inpatient Sample Database
Bibliographic record
Abstract
Background: Inflammatory bowel disease (IBD) has been associated with increased risk of developing pancreatitis. We analyzed data from the National Inpatient Sample (NIS) with the aim of evaluating the outcomes of acute pancreatitis (AP) in patients with co-existent Crohn's disease (CD) or ulcerative colitis (UC). Methods: This was a cross-sectional study using the 2020 NIS database. Patients were included if they were more than 18 years old with a principal diagnosis of AP. Main outcome measurements of our study were in-hospital mortality, length of hospital stay, hospital total charges, incidences of hypovolemic shock, severe sepsis with and without shock, acute kidney failure (AKI), and the need for intensive care unit (ICU) care. Statistical analyses were performed on STATA version 18.0. Results: There were 258,965 (0.8%) admissions with the primary diagnosis of AP among the 32 million discharges in 2020 NIS database. Among patients with AP, a total of 1,930 (0.75%) and 1,170 (0.45%) hospitalizations had co-existing CD and UC, respectively. The overall in-hospital mortality for AP was 1,560 (0.62%). Patients with UC hospitalized for AP had increased odds of in-hospital mortality (adjusted odds ratio (aOR): 3.62, 95% confidence interval (CI): 1.310 - 9.978, P = 0.013) while for patients with CD, there were no in-hospital mortality. Patients with CD had increased odds of developing comorbid AKI (aOR: 1.37, 95% CI: 1.005 - 1.880, P = 0.047) when they present with AP but not those with UC. Conclusions: Patients hospitalized with AP had increased odds of in-hospital mortality and comorbid AKI when they have co-existent UC and CD, respectively.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".