Predicting Risk of End-Stage Liver Disease in Antiretroviral-Treated Human Immunodeficiency Virus/Hepatitis C Virus-Coinfected Patients
Bibliographic record
Abstract
Background. End-stage liver disease (ESLD) is an important cause of morbidity among human immunodeficiency virus (HIV)/hepatitis C virus (HCV)-coinfected patients. Quantifying the risk of this outcome over time could help determine which coinfected patients should be targeted for risk factor modification and HCV treatment. We evaluated demographic, clinical, and laboratory variables to predict risk of ESLD in HIV/HCV-coinfected patients receiving antiretroviral therapy (ART). Methods. We conducted a retrospective cohort study among 6016 HIV/HCV-coinfected patients who received ART within the Veterans Health Administration between 1997 and 2010. The main outcome was incident ESLD, defined by hepatic decompensation, hepatocellular carcinoma, or liver-related death. Cox regression was used to develop prognostic models based on baseline demographic, clinical, and laboratory variables, including FIB-4 and aspartate aminotransferase-to-platelet ratio index, previously validated markers of hepatic fibrosis. Model performance was assessed by discrimination and decision curve analysis. Results. Among 6016 HIV/HCV patients, 532 (8.8%) developed ESLD over a median of 6.6 years. A model comprising FIB-4 and race had modest discrimination for ESLD (c-statistic, 0.73) and higher net benefit than alternative strategies of treating no or all coinfected patients at relevant risk thresholds. For FIB-4 >3.25, ESLD risk ranged from 7.9% at 1 year to 26.0% at 5 years among non-blacks and from 2.4% at 1 year to 14.0% at 5 years among blacks. Conclusions. Race and FIB-4 provided important predictive information on ESLD risk among HIV/HCV patients. Estimating risk of ESLD using these variables could help direct HCV treatment decisions among HIV/HCV-coinfected patients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".