Updated thresholds for alanine aminotransferase do not exclude significant histological disease in chronic hepatitis C
Bibliographic record
Abstract
BACKGROUND AND AIM: Histological changes in hepatitis C virus (HCV)-infected patients with persistently normal alanine aminotransferase (PNALT) have not been evaluated for updated upper limits of normal (ULN; ≤ 19/30 U/L for females/males). We assessed significant fibrosis (≥ F2, METAVIR) in patients with PNALT and persistently elevated alanine aminotransferase (PEALT). PATIENTS AND METHODS: Nine hundred and twenty consecutive, unselected HCV patients were stratified into four groups: Group I: (n = 124) PNALT within the updated ULN [0.5 × ULN (corresponding to ≤ 19 U/L) for females; 0.75 × ULN (corresponding to ≤ 30 U/L) for males]; Group II (n = 173): PNALT ≤ 1 × ULN but greater than Group I; Group III (n = 313): PEALT 1-2 × ULN; and Group IV (n = 310): PEALT > 2 × ULN. PNALT was defined as ≥ 3 determinations within the normal range over ≥ 6 months. RESULTS: Advanced ≥ F3 and ≥ F2 fibrosis increased incrementally across Groups I; II; III; and IV: 24.2 and 45.2%; 25.4 and 56.1%; 36.1 and 64.2%; and 50 and 77.1% respectively (P<0.0001 for both). Multivariable logistic regression analysis identified age [odds ratio (OR), 1.05; 95% confidence intervals (CI): 1.02-1.08; P<0.0001], alanine aminotransferase (ALT) groups (OR 1.38; 95% CI: 1.03-1.83; P = 0.030), presence of moderate-severe steatosis (OR 2.70; 95% CI: 1.19-6.15; P = 0.018) and ≥ A2 necroinflammation (OR 17.9; 95% CI: 8.88-36.20; P < 0.0001) as independent predictors of ≥ F2 fibrosis. Updated ULN for ALT were better at excluding ≥ F2 fibrosis compared with traditional ULN (90.6 vs. 74.2%, P = 0.0041) but less specific (20.8 vs. 44%, P = 0.0007) with similar positive/negative predictive values. CONCLUSIONS: HCV patients with 'updated' normal ALT have the lowest prevalence of significant fibrosis, although utilizing these levels without resorting to biopsy would miss significant fibrosis in almost one-half of such 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".