Historical features are poor predictors of liver fibrosis in Canadian patients with chronic hepatitis C
Bibliographic record
Abstract
The progression of fibrosis in chronic hepatitis C infection (HCV) is related to host factors including age, gender and alcohol consumption. Due to the morbidity and potential mortality of liver biopsy, a noninvasive method of assessing hepatic fibrosis is needed. The aim of this study was to assess the utility of historical features in predicting fibrosis using published rates of fibrosis progression. The charts of 239 untreated patients with HCV were reviewed; patients who had a liver biopsy and whose duration of infection could be estimated (n=106) were categorized according to gender, age at infection (< or = or > 40 years) and peak alcohol consumption (< or > or = 50 g/day). Estimates of fibrosis were calculated using the product of the interval between infection and biopsy and published rates of fibrosis progression. Estimates were compared with liver biopsies staged according to the Metavir system (F0-F4; F0=no fibrosis; F4= cirrhosis). The mean age of patients was 42 +/- 8 years, 61% were male and 36% consumed > 50 g of alcohol daily. The mean duration of infection was 19 +/- 9 years (range, 1-40) and ALT was elevated > 1.5 times upper normal in 63%. When patients were classified into those with mild (F0-F2) and severe (F3-F4) fibrosis, the sensitivity, specificity, positive predictive value and negative predictive value of an estimate of mild fibrosis was 60%, 55%, 78% and 34%, respectively. An estimate of severe fibrosis had a sensitivity of 55%, specificity of 60%, positive predictive value of 34% and negative predictive value of 78%. Agreement between fibrosis estimates and actual histological stages was poor (kappa = 0.13, P=0.08). The prediction of hepatic fibrosis in HCV infection using historical features and published rates of fibrosis progression is poor in a Canadian clinical practice setting. Alternate noninvasive methods of predicting hepatic fibrosis are needed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".