Fulminant hepatic failure in association with quetiapine: a case report
Bibliographic record
Abstract
INTRODUCTION: Fulminant hepatic failure is a serious disease with significant mortality and morbidity. Identifying the exact cause of hepatic failure and predicting prognosis is of paramount importance in managing such patients. Drug-induced liver injury is a common but challenging entity to treat. The use of newer drugs and medications with previously unknown hepatotoxicity add to the challenges faced by treating physicians. Quetiapine is an antipsychotic that has rarely been linked to acute liver injury. In the present work we describe a case of fulminant hepatic failure secondary to use of quetiapine. CASE PRESENTATION: A 59-year-old Caucasian woman with known Parkinson's disease was being treated with quetiapine for hallucinations. She was referred to our hospital with yellow discoloration of the sclera and later on developed clinical features suggestive of hepatic encephalopathy. A diagnosis of fulminant hepatic failure was made following her admission to the intensive care unit. Her condition improved after discontinuing the drug and providing the standard supportive treatment. CONCLUSIONS: Our findings in the present report emphasize the importance of keeping an open mind in cases of fulminant hepatic failure. As drug-induced hepatotoxicity is the most common cause of fulminant hepatic failure in many parts of the world, consideration should be given to the medication(s) patients receive as the potential cause and a review of this list should be part of the clinical care given.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| 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".