Acute Liver Injury in a Patient Treated With Rosuvastatin: A Rare Adverse Effect
Bibliographic record
Abstract
Drug-induced liver injury (DILI) is among the challenging liver conditions encountered by clinicians today. It has a low incidence in the general population with an approximated annual incidence of 10 - 15 cases per 10,000 - 100,000 persons who have taken prescription medications. Nevertheless, DILI remains the most frequent cause of acute liver injury in the United States. Rosuvastatin is a commonly prescribed medication that, similar to other statins, is associated with serum aminotransferase elevations that are mild, asymptomatic and usually self-limited. Here, we report a case of a man who developed acute liver injury after taking rosuvastatin for hypercholesterolemia treatment. Moreover, DILI with autoimmune features represents a key subgroup of hepatotoxicity attributable to medication exposure. Similar to idiopathic autoimmune hepatitis, circulating autoantibodies and a hypergammaglobulinemia are often present in the serum of such individuals. However, such findings are not invariable. In the case reported here, these laboratory features were absent, but a liver biopsy demonstrated interface hepatitis with a prominent plasma cell infiltrate, histologic components consistent with an immune-mediated drug reaction. After withdrawal of the offending medication did not result in complete resolution, corticosteroid therapy was administered with a subsequent clinical response, confirming the diagnosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".