Reactivation of Hepatitis B Virus after Withdrawal of Erlotinib
Bibliographic record
Abstract
Reactivation of hepatitis B virus (hbv) is a reported complication for patients undergoing chemotherapy, particularly immunochemotherapy with anti-CD20 agents such as rituximab. However, as the use of molecularly targeted agents increases, the risk of viral reactivation is less clearly defined. Here, we present the case of a 62-year-old woman with newly diagnosed EGFR mutation-positive metastatic non-small-cell lung cancer (nsclc). Per interview, our patient had a remote history of hbv infection. She was started on erlotinib and developed profound diarrhea leading to renal failure that required hospital admission and temporary discontinuation of erlotinib. At 8 days after erlotinib cessation, she had a marked spike in her liver function tests, with viral serologies that were consistent with hbv reactivation. Although erlotinib and other tyrosine kinase inhibitors (tkis) are not classically associated with hbv reactivation, hbv reactivation can occur even in the setting of tki withdrawal. Before tki initiation, careful patient screening in those at risk for hbv should be performed to attenuate preventable hepatotoxicity and to differentiate between other causes of hepatotoxicity (for example, drug-induced toxicity).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".