Slower Fibrosis Progression Among Liver Transplant Recipients With Sustained Virological Response After Hepatitis C Treatment
Bibliographic record
Abstract
BACKGROUND: The natural course of hepatic fibrosis in HCV allograft recipients with sustained virological response (SVR) after anti-HCV therapy remains debatable. The aim of this study was to examine the progression of fibrosis in a cohort of patients who achieved SVR compared with those without treatment. METHODS: The 167 patients who met the inclusion and exclusion criteria were chosen from a transplant database. All patients were required to have histological evidence of recurrent HCV infection post-liver transplantation and a follow-up biopsy. The 140 of these patients had received anti-viral therapy. Twenty-seven patients were identified as controls and were matched with the treatment group in all respects. The patients were categorized into four groups based on treatment response: 1) no treatment (control) (n = 27); 2) non-responders (n = 81); 3) relapsers (n = 32); and 4) SVR (n = 27). The endpoint was the stage of fibrosis on the follow-up liver biopsy. RESULTS: The treated and untreated groups were similar in clinical characteristics at the time of transplantation and prior to the initiation of treatment. The 72% of the cohort showed a fibrosis progression of ≥ 1 stage; this change did not significantly differ between the patient groups. Nonetheless, the fibrosis progression rate was the highest in the untreated group and lowest in the patients who achieved SVR. A coefficient of determination was used. Improvements in fibrosis scores were found with greater treatment duration. These improvements were most evident with the achievement of SVR. CONCLUSIONS: In conclusion, SVR after anti-viral therapy for recurrent hepatitis C infection post-transplantation was associated with slower fibrosis progression and significantly improved graft survival.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".