Alcohol as a risk factor for pancreatitis. A systematic review and meta-analysis.
Bibliographic record
Abstract
CONTEXT: Epidemiologic studies have suggested an association between alcohol consumption and pancreatitis, although the exact dose-response relationship is unknown. It also remains uncertain whether a threshold effect exists. OBJECTIVE: To conduct a systematic review and meta-analysis of epidemiologic studies on the association between alcohol consumption and the risk of pancreatitis. METHODS: We searched Ovid MEDLINE, EMBASE, CINAHL, Web of Science, ETOH and AIM. Studies were included if they reported quantifiable information on risk and related confidence intervals with respect to at least three different levels of alcohol intake. RESULTS: Six studies, including 146,517 individuals with 1,671 cases of pancreatitis, met the inclusion criteria. We found a monotonic and approximately exponential dose-response relationship between average volume of alcohol consumption and pancreatitis. However, in a categorical analysis the lower drinking categories were not significantly elevated, with an apparent threshold of 4 drinks daily. CONCLUSIONS: As the available evidence also indicates that the relationship is biologically plausible, these results support the existence of a link between alcohol consumption and the risk of pancreatitis.
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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.021 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".