Learning from a rare phenomenon — spontaneous clearance of chronic hepatitis C virus post-liver transplant: A case report
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) can lead to chronic liver damage resulting in cirrhosis and hepatocellular carcinoma. Spontaneous clearance of HCV has been documented after an acute infection in 20%-45% of individuals. However, spontaneously resolved chronic hepatitis C following liver transplant (LT) is rare and has been documented only in a few case reports. The phenomenon of spontaneous clearance of chronic hepatitis C occurs together with other meaningful events, which are typically associated with significant changes in the host immunity. CASE SUMMARY: We report three cases of spontaneous resolution of chronic hepatitis C following liver transplantation. These patients either failed or had no HCV treatment prior to transplant, but had spontaneous resolution of HCV post-LT as documented by undetectable polymerase chain reaction (PCR). Diagnosis of HCV was based on viremia through PCR or liver biopsy. All three patients currently undergo surveillance and have no recurrence of HCV. CONCLUSION: Examining each patient's clinical course, we learned about many viral, host and cellular-factors that may have enhanced the host's immunity leading to spontaneous clearance of HCV. Though HCV treatment has excellent cure rates, understanding this mechanism may provide clinicians with insights regarding timing and duration of treatment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.005 |
| 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".