Screening for nonalcoholic steatohepatitis by using cytokeratin 18 and transient elastography in HIV mono-infection
Bibliographic record
Abstract
BACKGROUND AND AIM: HIV-infected individuals are at high risk of developing nonalcoholic steatohepatitis (NASH), a leading cause of end-stage liver disease in Western countries. Nonetheless, due to the invasiveness of liver biopsy, NASH remains poorly understood in HIV mono-infection. We aimed to characterize the prevalence and predictors of NASH in unselected HIV mono-infected patients by means of non-invasive diagnostic tools. METHODS: HIV-infected adults without significant alcohol intake or co-infection with hepatitis B or C underwent a routine screening program employing transient elastography (TE) with controlled attenuation parameter (CAP) and the serum biomarker cytokeratin-18 (CK-18). NASH was diagnosed non-invasively as the coexistence of fatty liver (CAP ≥248 dB/m) and CK-18 >246 U/L. Identified cases of NASH were offered a diagnostic liver biopsy. Predictors of NASH were determined by multivariate logistic regression analysis. RESULTS: 202 consecutive HIV mono-infected patients were included. NASH was non-invasively diagnosed in 23 cases (11.4%). Among them, 17 underwent a liver biopsy, and histology confirmed NASH in all cases. The prevalence of NASH was higher in patients with hypertriglyceridemia (17.1%), insulin resistance defined by homeostasis model for assessment of insulin resistance (HOMA-IR) (25%), those with detectable HIV viral load (42.9%) and those with elevated ALT (53.6%). After adjustment, higher HOMA-IR (adjusted odds ratio [aOR] = 1.20, 95% CI 1.01-1.43; p = 0.03) and ALT (aOR = 2.39, 95% CI 1.50-3.79; p<0.001) were independent predictors of NASH. CONCLUSIONS: NASH, diagnosed by a non-invasive diagnostic approach employing CK-18 and TE with CAP, is common in unselected HIV mono-infected individuals, particularly in the presence of insulin resistance and elevated ALT.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".