Association of Cannabis Use with Pancreatitis
Bibliographic record
Abstract
Introduction: Acute and chronic pancreatitis (AP & CP) are painful inflammatory disorders causing substantial public health burden. The etiology of pancreatitis is clustered into three groups: alcohol, gallstones, and others (non-gallstones-non-alcohol). Containing potent anti-inflammatory cannabinoids, cannabis use (CU) could suppress pancreatitis, has shown in murine models. Furthermore, conflicting results are reported in other studies where cannabinoids have aggravated pancreatitis. However, human data are lacking, besides few reported cases. Therefore, we conducted a more extensive and detailed assessment of the impact of CU on pancreatitis. Methods: We collected data from 2012-2014 of the Healthcare Cost and Utilization Project - Nationwide Inpatient Sample (NIS) discharge records of patients 18-years and older, and identified three populations: those with gallstones (379,125); abusive alcohol drinkers (762,356); and non-gallstones-non-alcohol users (15,255,464). Within each population, CUsers were identified and matched by age, race, and gender to records without CU. Adjusted Odds Ratio (AOR) of having AP and CP were measured with conditional logistic models (SAS 9.4). Results: Concomitant cannabis among abusive alcohol use was associated with reduced odds of AP and CP (aOR: 0.50[0.48-0.53] & 0.77[0.71-0.84]). Strikingly, for individuals with gallstones, additional CU did not impact the incidence of AP or CP. Amongst non-gallstones-non-alcohol users, CU was associated with increased likelihood of CP, but not AP (1.28[1.14-1.44] & 0.93[0.86-1.01]). Conclusion: Our findings suggest reduced odds of only alcohol-associated pancreatitis with CU. Our results emphasize the urgent need for further studies to elucidate the modulatory effect of cannabis on pancreatitis among different risk groups.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".