A multicentre study of the usefulness of liver biopsy in hepatitis C
Bibliographic record
Abstract
The role of liver biopsy in the assessment of chronic hepatitis C is generally accepted yet there is no prospective data available to quantify its contribution. A previous single centre pilot study suggested that the clinician could predict the amount of fibrosis and to a lesser extent, inflammation with moderate accuracy. The 2002 National Institute of Health Hepatitis C Consensus Conference recommended further study of the role of liver biopsy. Our objective was to compare a prediction of biopsy findings by expert clinicians using usually available clinical and laboratory data to actual biopsy results in order to determine whether biopsy is required routinely. This was a prospective observational study conducted at seven university centres in which the accuracy of clinician's predictions of the degree of inflammation and fibrosis were compared with the actual liver biopsy using an adaptation of a standard histological scoring system. We studied 81 adults with previously untreated chronic hepatitis C, raised serum transaminases and positive HCV-RNA in serum. Clinicians predicted the inflammatory grade in 44 of 80 cases (55%) and the fibrosis stage in 46 of 81 cases (57%). Nine of 17 cirrhotic cases were predicted (sensitivity 53%, specificity 56%). No unexpected additional diagnoses were made on the biopsies. Thus despite knowledge of the clinical and laboratory investigations of patients with hepatitis C, clinicians are unable to accurately predict the hepatic inflammatory grade and fibrotic stage. Liver biopsy is an essential investigation to accurately evaluate the grade and stage of liver disease patients with hepatitis C.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".