Acute hepatitis following mRNA BNT162b2 COVID-19 vaccination
Bibliographic record
Abstract
BACKGROUND: Widespread administration of COVID-19 vaccinations have led to reports of rare but potentially serious side effects. METHODS: We present two cases of acute hepatitis following mRNA BNT162b2 (Comirnaty, Pfizer-BioNTech) vaccination. RESULTS: A 25-year-old male presented to hospital with progressive jaundice 5 days following his second dose of Comirnaty. Initial bloodwork revealed severe hepatocellular enzyme elevation and conjugated hyperbilirubinemia with preserved INR. Extensive serologic workup was negative, with normal imaging. Percutaneous liver biopsy was performed and revealed acute cholestatic hepatitis possibly related to drug-induced liver injury. He was started on prednisone 40 mg daily with good initial response but had a second flare; a biopsy was repeated which showed near-identical findings. Steroids were discontinued given non-response and the patient had gradual near complete resolution of liver enzymes and hyperbilirubinemia. A 32-year-old male presented with a 4-week history of nausea followed by progressive choluria, jaundice, and pruritis. He received his second dose of Comirnaty vaccination two weeks prior to presentation. Initial bloodwork showed mixed enzyme elevation with hyperbilirubinemia. Serological workup and imaging were unrevealing. He underwent liver biopsy which showed severe intrahepatic cholestasis, with drug-induced liver injury being suggested as most likely cause. His course was self-limited with resolution of serological abnormalities and symptoms. CONCLUSIONS: While overwhelmingly safe on a population level, our case series illustrate two cases of acute icteric hepatitis following mRNA BNT162b2 vaccination. Clinicians should be aware of this association with hepatic inflammation and consider vaccine history an important component of evaluating patients with acute liver injury.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".