Progression of Liver Fibrosis and Modern Combination Antiretroviral Therapy Regimens in HIV-Hepatitis C–Coinfected Persons
Bibliographic record
Abstract
BACKGROUND: Liver diseases progress faster in human immunodeficiency virus (HIV)-hepatitis C virus (HCV)-coinfected persons than HIV-monoinfected persons. The aim of this study was to compare rates of liver fibrosis progression (measured by the aspartate-to-platelet ratio index [APRI]) among HIV-HCV-coinfected users of modern protease inhibitor (PI)- and nonnucleoside reverse transcriptase inhibitor (NNRTI)-based regimens with a backbone of tenofovir/emtricitabine (TDF/FTC) or abacavir/lamivudine (ABC/3TC). METHODS: Data from a Canadian multicenter cohort study were analyzed, including 315 HCV polymerase chain reaction-positive persons who initiated antiretroviral therapy with a PI or NNRTI and a backbone containing either TDF/FTC or ABC/3TC. Multivariate linear regression analyses with generalized estimating equations were performed after propensity score matching to balance covariates across classes of anchor agent. RESULTS: A backbone of TDF/FTC was received by 67% of PI users and 69% of NNRTI users. Both PI and NNRTI use was associated with increases in APRI over time when paired with a backbone of ABC/3TC: 16% per 5 years (95% confidence interval [CI], 4%, 29%) and 11% per 5 years (95% CI, 2%, 20%), respectively. With TDF/FTC use, no clear association was found among PI users (8% per 5 years, 95% CI, -3%, 19%) or NNRTI users (3% per 5 years, 95% CI, -7%, 12%). CONCLUSIONS: Liver fibrosis progression was more influenced by the backbone than by the class of anchor agent in HIV-HCV-coinfected persons. Only ABC/3TC-containing regimens were associated with an increase of APRI score over time, regardless of the class of anchor agent used.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".