Protease inhibitor-induced acute pancreatitis in post liver transplant hepatitis C Patients
Bibliographic record
Abstract
Drug induced pancreatitis (DIP) is a serious adverse effect of many commonly used drugs. Pegylated interferon (peg- IFN) and ribavirin used for treatment of chronic hepatitis C (CHC) infection and various protease inhibitors (PIs) such as indinavir, nelfinavir, ritonavir and saquinavir used for HIV infection have been reported to cause DIP; although the mechanism of pancreatitis is not well known. Recently, telaprevir and boceprevir are introduced for treatment of HCV genotype 1 infection along with peg-IFN and ribavirin. There are no reports of acute pancreatitis due to telaprevir and boceprevir in liver transplant setting. We managed two such cases; both were male with HCV genotype 1 infection, had living donor liver transplantation for hepatocellular cancer few years ago and stable on cyclosporine. Both developed AP a month after adding one of PI to their combination therapy. First patient had past history of partial response with peg-IFN and ribavirin and retreated with addition of telaprevir to combination therapy. Second patient received peg-IFN and ribavirin for 4 months and then boceprevir was added. Patients were managed conservatively, the culprit PI was stopped and they recovered. We used the Naranjo Probability Scale for Adverse Drug Events to estimate the probability that a drug was the cause of the acute pancreatitis. A score of 7 was calculated in both patients, indicating a probable adverse drug reaction. The algorithm devised by Trivedi et al to diagnose drug-induced pancreatitis was also used and confirmed that this was likely to be a drug reaction. There is adequate circumstantial evidence pointing to telaprevir and boceprevir as the cause of their acute pancreatitis. Further evidence is needed but in the meantime we would recommend routine monitoring of amylase levels for all patients on triple therapy and advise patients of potential symptoms for which they should seek medical advice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".