Reduced Risk of Alcohol‐Induced Pancreatitis With Cannabis Use
Bibliographic record
Abstract
BACKGROUND: Pancreatitis is an increasingly common clinical condition that causes significant morbidity and mortality. Cannabis use causes conflicting effects on pancreatitis development. We conducted a larger and more detailed assessment of the impact of cannabis use on pancreatitis. METHODS: We analyzed data from 2012 to 2014 of the Healthcare Cost and Utilization Project-Nationwide Inpatient Sample discharge records of patients 18 years and older. We used the International Classification of Disease, Ninth Edition codes, to identify 3 populations: those with gallstones (379,125); abusive alcohol drinkers (762,356); and non-alcohol-non-gallstones users (15,255,464). Each study population was matched for cannabis use record by age, race, and gender, to records without cannabis use. The estimation of the adjusted odds ratio (aOR) of having acute and chronic pancreatitis (AP and CP) made use of conditional logistic models. RESULTS: Concomitant cannabis and abusive alcohol use were associated with reduced incidence of AP and CP (aOR: 0.50 [0.48 to 0.53] and 0.77 [0.71 to 0.84]). Strikingly, for individuals with gallstones, additional cannabis use did not impact the incidence of AP or CP. Among non-alcohol-non-gallstones users, cannabis use was associated with increased incidence of CP, but not AP (1.28 [1.14 to 1.44] and 0.93 [0.86 to 1.01]). CONCLUSIONS: Our findings suggest a reduced incidence of only alcohol-associated pancreatitis with cannabis use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".