Liver Fibrosis Regression in People Living With HIV After Successful Treatment for Hepatitis C
Bibliographic record
Abstract
BACKGROUND: Successful treatment of hepatitis C virus (HCV) can lead to liver fibrosis regression. It is not known who will experience fibrosis regression or how quickly it will occur. METHODS: We modeled transient elastography (TE) measurements from 1470 HIV-HCV coinfected participants followed in cohorts contributing data to InCHEHC, an international collaboration. Participants were eligible if they had at least 1 TE measurement in the year before starting a successful direct-acting antiviral treatment for HCV. This measurement was used to classify participants into 1 of 3 fibrosis subgroups. We analyzed measurement sequences in each subgroup using a covariate-adjusted generalized additive mixed model, with an adaptive spline representing changes in the mean measurement before, during, and after treatment. RESULTS: Each fibrosis subgroup had a distinctly different response. Most participants with cirrhosis (F4, TE ≥14.6 KPa) before HCV treatment did not show meaningful fibrosis regression-approximately 70% were predicted to remain >12 KPa 3 years after treatment ended. Participants with significant fibrosis (F2-F3, TE ≥7.2 and <14.6 KPa) showed appreciable regression in the first 2 years after treatment, falling on average to levels <7.2 KPa. Those without fibrosis before treatment (F0-F1) did not progress. CONCLUSIONS: Most coinfected people with cirrhosis before HCV cure will remain cirrhotic. For those with significant fibrosis, regression can be expected within 2 years to levels not normally associated with an increased risk of end-stage liver disease. A TE measurement 2 years after cure should give a reliable estimate of residual fibrosis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".