A Population-Based Assessment of the Burden of Acute Pancreatitis in the United States
Bibliographic record
Abstract
OBJECTIVES: The aim of this study is to investigate the incidence and mortality of emergency department (ED) visits in the United States attributed to acute pancreatitis (AP) and quantify predictors of admission and mortality. METHODS: Using the nationwide ED sample, all ED visits between 2006 and 2009 for AP were extracted. Multivariable analyses were fitted for prediction of admission and mortality. RESULTS: A weighted sample of 1,224,121 patient visits with AP was captured. Of those, 75.4% resulted in admission and 0.7% died. Between 2006 and 2009, the incidence of AP ED visits increased from 9.9 to 10.6 per 10,000 person-years. Patients were more likely to be admitted if sicker (Charlson Comorbidity Index score ≥ 3; OR, 6.48; P < 0.001) and if the etiology of pancreatitis was alcoholic versus biliary (OR, 2.20; P < 0.001). They were more likely to die if sicker (Charlson Comorbidity Index score ≥ 3; OR, 1.51; P < 0.001) and covered with Medicare or Medicaid versus private insurance (OR, 1.40; P < 0.001 and OR, 1.45; P < 0.001, respectively). CONCLUSIONS: Emergency department visits for AP represent a significant burden on US health care. Although mortality is lower than previously reported, significant disparities exist in patients presenting with AP with regard to admission and mortality rates. Further investigations are needed to assess these disparities.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".