Application of guidelines for the management of nonalcoholic fatty liver disease in three prospective cohorts of <scp>HIV</scp>‐monoinfected patients
Bibliographic record
Abstract
OBJECTIVES: Current guidelines recommend use of a diagnostic algorithm to assess disease severity in cases of suspected nonalcoholic fatty liver disease (NAFLD). We applied this algorithm to HIV-monoinfected patients. METHODS: We analysed three prospective screening programmes for NAFLD carried out in the following cohorts: the Liver Disease in HIV (LIVEHIV) cohort in Montreal, the Modena HIV Metabolic Clinic (MHMC) cohort and the Liver Pathologies in HIV in Palermo (LHivPa) cohort. In the LIVEHIV and LHivPa cohorts, NAFLD was diagnosed if the controlled attenuation parameter (CAP) was ≥ 248 dB/m; in the MHMC cohort, it was diagnosed if the liver/spleen Hounsfield unit (HU) ratio on abdominal computerized tomography scan was < 1.1. Medium/high-risk fibrosis category was defined as fibrosis-4 (FIB-4) ≥ 1.30. Patients requiring specialist referral to hepatology were defined as either having NAFLD and being in the medium/high-risk fibrosis category or having elevated alanine aminotransferase (ALT). RESULTS: A total of 1534 HIV-infected adults without significant alcohol intake or viral hepatitis coinfection were included in the study. Of these, 313 (20.4%) patients had the metabolic comorbidities (obesity and/or diabetes) required for entry in the diagnostic algorithm. Among these patients, 123 (39.3%) required specialist referral to hepatology, according to guidelines. A total of 1062 patients with extended metabolic comorbidities (any among obesity, diabetes, hypertension and dyslipidaemia) represented most of the cases of NAFLD (79%), elevated ALT (75.9%) and medium/high-risk fibrosis category (75.4%). When the algorithm was extended to these patients, it was found that 341 (32.1%) would require specialist referral to hepatology. CONCLUSIONS: According to current guidelines, one in five HIV-monoinfected patients should undergo detailed assessment for NAFLD and disease severity. Moreover, one in ten should be referred to hepatology. Expansion of the algorithm to patients with any metabolic comorbidities may be considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".