Predicting the liver histology in chronic hepatitis C: how good is the clinician?
Bibliographic record
Abstract
Liver biopsy is believed to be necessary before antiviral treatment in hepatitis C. Studies have found symptoms and biochemistry poorly predictive of grade and stage. In practice, a combination of factors is used to anticipate histology. The aim of this study is to evaluate the ability of global clinical assessment to predict histology in hepatitis C.Fifty-four consecutive patients referred to a university center for consideration of antiviral therapy were enrolled. Clinical and laboratory data were recorded as was a prediction of the inflammatory grade (0-3) and fibrotic stage (0-3), with fibrotic stage 3 referring to cirrhosis. Liver biopsies were read by a blinded pathologist. The predictive value of the clinical assessment and individual parameters was assessed.All predictions were < or = 1 point off the actual grade and stage. Thirty-six (66.7%) patients' grades and 41 (75.9%) patients' stages were exactly predicted. All four cirrhotic patients (sensitivity 100%, specificity 94%) and one case of hemochromatosis were correctly predicted. Spider nevi, organomegaly, white blood cell count < or = 4 x 10(9)/L, ALT > 120 U/L, bilirubin > 20 micromol/L, albumin < or = 35 g/L, and ferritin > 200 microg/L predicted grade > or =2. Stage > or =2 was associated with age > 40 yr, previous decompensation, spider nevi, organomegaly, white blood cell count < or = 4 x 10(9)/L, albumin < or = 35 g/L, platelets < or = 150 x 10(9)/L, and international normalized ratio > 1.2. Grade correlated with stage (Spearman coefficient = 0.54, p < 0.001). By multivariate analysis, ferritin plus spider nevi or hypoalbuminemia was independently predictive of inflammation. Spider nevi and thrombocytopenia, with either splenomegaly or hypoalbuminemia, were useful three-variable models for predicting fibrosis. The corresponding scoring systems produced useful likelihood ratios.Global clinical assessment mirroring clinical practice in a tertiary liver transplant center is moderately accurate in predicting grade and stage in hepatitis C. Liver biopsy is the current gold standard; however, the amount of new information gleaned is less than was perceived. The need for routine biopsy before antiviral treatment in hepatitis C should be reevaluated in a multicenter study.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".