Relationship between Gout and Diabetes Mellitus after Acute Pancreatitis: A Nationwide Cohort Study
Bibliographic record
Abstract
OBJECTIVE: After acute pancreatitis, individuals often have low-grade inflammation, and subsequently develop metabolic sequelae such as post-pancreatitis diabetes mellitus (PPDM). Although numerous studies have investigated the relationship between gout and type 2 diabetes, little is known about the relationship between gout and PPDM. The aim was to investigate the associations between gout and PPDM. METHODS: Using nationwide pharmaceutical dispensing data linked to hospital discharge data in New Zealand, gout and PPDM were identified among individuals after first episode of acute pancreatitis between January 1, 2007, and December 31, 2015. Multivariable Cox regression analyses were conducted, adjusting for age, sex, ethnicity, social deprivation index, alcohol consumption, tobacco smoking, comorbidities, medications (glucocorticoids, statins, and estrogens), and characteristics of acute pancreatitis. RESULTS: A total of 10,117 individuals were included in the analysis of risk for gout and 9471 in the analysis of risk for PPDM. PPDM was significantly associated with a higher risk of gout in the overall cohort (adjusted HR 1.88, 95% CI 1.15-3.06) and women (2.72, 95% CI 1.31-5.65), but not in men (1.42, 95% CI 0.73-2.78). Preexisting gout was significantly associated with a higher risk of PPDM in the overall cohort (adjusted HR 1.58, 95% CI 1.04-2.41) and women (2.66, 95% CI 1.29-5.49), but not in men (1.31, 95% CI 0.78-2.20). CONCLUSION: The relationship between gout and PPDM is bidirectional in the post-pancreatitis setting. A history of gout is a risk factor of PPDM, particularly in women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".