Incidence and Predictors of Hepatic Steatosis and Fibrosis by Serum Biomarkers in a Large Cohort of Human Immunodeficiency Virus Mono-Infected Patients
Bibliographic record
Abstract
Background. Longitudinal data on liver disease in human immunodeficiency virus (HIV) mono-infection are scarce. We used noninvasive serum biomarkers to study incidence and predictors of hepatic steatosis and fibrosis. Methods. Hepatic steatosis was diagnosed by hepatic steatosis index ≥36. Advanced liver fibrosis was diagnosed by fibrosis-4 index >3.25. Kaplan-Meier analysis was used to estimate incidences. Cox regression analysis was used to explore predictors of hepatic steatosis and fibrosis development. Results. In this retrospective observational study, 796 consecutive HIV mono-infected patients were observed for a median of 4.9 (interquartile range, 2.2-6.4) years. Incidence of hepatic steatosis was 6.9 of 100 per person-years (PY) (95% confidence interval [CI], 5.9-7.9). Incidence of advanced liver fibrosis was 0.9 of 100 PY (95% CI, 0.6-1.3). Development of hepatic steatosis was predicted by black ethnicity (adjusted hazard ratio [aHR] = 2.18; 95% CI, 1.58-3; P < .001) and lower albumin (aHR = 0.94; 95% CI, 0.91-0.97; P < .001). Development of advanced liver fibrosis was predicted by higher glucose (aHR = 1.22; 95% CI, 1.2-1.3; P < .001) and lower albumin (aHR = 0.89; 95% CI, 0.84-0.93; P < .001). Conclusions. Incident hepatic steatosis is frequent in HIV mono-infected patients, particularly in those of black ethnicity. These patients can also develop advanced liver fibrosis. Identification of at-risk individuals can help early initiation of hepatological monitoring and interventions, such as targeting euglycemia.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".