A271 ETIOLOGICAL CHARACTERIZATION OF DRUG-INDUCED ACUTE PANCREATITIS
Bibliographic record
Abstract
Abstract Background Drugs are considered a relatively rare and understudied cause of acute pancreatitis (AP). The lack of convincing and conclusive data on drug-induced AP (DIAP) complicates the diagnosis as well as the identification of the causative drug. Aims We hypothesized that documenting the causes of DIAP in the Saguenay-Lac-Saint-Jean (SLSJ) will add new evidence to the current literature and will provide a comprehensive picture of this health condition in the region. The aim of this study is to document causes of DIAP cases that occurred in the SLSJ population. Methods We have conducted a retrospective and descriptive population-based study of DIAP cases that occurred between 2006 and 2014 in the six hospitals deserving the SLSJ entire population. Cases were selected from the Quebec Ministry of Health hospitalizations registry (MED-ECHO) administrative public database. Medical chart review was performed in attempt to characterize DIAP hospitalizations and to identify the imputable drugs. Results During the studied period, 75 cases (30.7% male, 69.3% female) were included totalling 90 hospitalizations for DIAP. Among them, 51 causative drugs were identified and were distributed in 17 different drug classes. Recurrent DIAP were documented in 13 cases and among them, 6 cases have experimented positive rechallenge. Six drugs (5-fluorouracil, atorvastatin, bortezomib, nilotinib, rosuvastatin and triamcinolone) were associated with the highest degree of evidence. The most common causative drugs of DIAP hospitalization were azathioprine (n = 7), followed by atorvastatin (n =6), hydrochlorothiazide (n = 5), rosuvastatin (n = 4) and codeine (n = 4). Conclusions This study has added new evidences about potentially pancreatitis-associated drugs in literature. This is the first study to report definite 5-fluorouracil and triamcinolone-induced AP. An updated version of the evidence-based literature review is needed to support the clinicians in the identification of the causative drugs. Funding Agencies None
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".