Liver Fibrosis in Human Immunodeficiency Virus (HIV)-Hepatitis C Virus (HCV) Coinfection Before and After Sustained Virologic Response: What Is the Best Noninvasive Marker for Monitoring Regression?
Bibliographic record
Abstract
BACKGROUND: Noninvasive markers of liver fibrosis such as aspartate aminotransferase-to-platelet ratio (APRI) and transient elastography (TE) have largely replaced liver biopsy for staging hepatitis C virus (HCV). As there is little longitudinal data, we compared changes in these markers before and after sustained virologic response (SVR) in human immunodeficiency virus (HIV)-HCV coinfected patients. METHODS: Participants from the Canadian Coinfection Cohort study who achieved SVR after a first treatment with either interferon/ribavirin or direct acting antivirals (DAAs), with at least 1 pre- and posttreatment fibrosis measure were selected. Changes in APRI or TE (DAA era only) were modeled using a generalized additive mixed model, assuming a gamma distribution and adjusting for sex, age at HCV acquisition, duration of HCV infection, and time-dependent body mass index, binge drinking, and detectable HIV RNA. RESULTS: Of 1981 patients, 151 achieved SVR with interferon and 553 with DAAs; 94 and 382 met inclusion criteria, respectively. In the DAA era, APRI increased (0.03 units/year; 95% credible interval (CrI): -.05, .12) before, declined dramatically during, and then changed minimally (-0.03 units/year; 95% CrI: -.06, .01) after treatment. TE values, however, increased (0.74 kPa/year; 95% CrI: .36, 1.14) before treatment, changed little by the end of treatment, and then declined (-0.55 kPa/year; 95% CrI: -.80, -.31) after SVR. CONCLUSIONS: TE should be the preferred noninvasive tool for monitoring fibrosis regression following cure. Future studies should assess the risk of liver-related outcomes such as hepatocellular carcinoma according to trajectories of fibrosis regression measured using TE to determine if and when it will become safe to discontinue screening.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".