Serum immunoglobulins predict the extent of hepatic fibrosis in patients with chronic hepatitis C virus infection
Bibliographic record
Abstract
Recently, we documented that immunoglobulins stimulate the proliferative activity of rat hepatic stellate cells in vitro. The aim of the present study was to determine whether there is any association between serum immunoglobulin levels and hepatic fibrosis in patients with chronic hepatitis C virus (HCV) infection. Charts from 116 patients with biochemical, serologic, virologic and histologic evidence of chronic hepatitis C infection and serum immunoglobulin levels (IgA, IgG, IgM and total) were reviewed. The mean (+/-SD) age of the study population was 46 +/- 11 years and 67 (58%) were male. There were significant correlations between serum IgA (r = 0.39, P = 0.00001), IgG (r = 0.49, P = 0.000002) and total (r = 0.51, P = 0.000003) immunoglobulin levels and the stage of hepatic fibrosis. When serum immunoglobulin levels were included into logistic regression analysis with variables known to be associated with advanced disease (male gender, age >40 years at onset of infection, duration of infection beyond 20 years and concurrent alcohol abuse) only IgA, IgG and total immunoglobulin levels (P < 0.05, <0.05 and <0.005, respectively) emerged as independent predictors of hepatic fibrosis. Our data indicate a strong association between serum immunoglobulin levels (IgA, IgG and total) and hepatic fibrosis in patients with HCV infection. This finding supports the need to further investigate whether immunoglobulins independently promote disease progression in patients with chronic HCV infection.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".