Role of cytokines in the assessment of the severity of chronic hepatitis C and the prediction of response to therapy.
Bibliographic record
Abstract
AIMS: (i) To characterize serum levels of pro/anti-inflammatory cytokines in non-cirrhotics with hepatitis C; (ii) to correlate levels of these cytokines with degree of disease at baseline; and (iii) to characterize the immuno-modulatory effects of therapy with response. METHODS: We studied 103 patients that were part of randomized, controlled, clinical trials. Serum cytokines were measured using enzyme-linked immunosorbent assay. RESULTS: Using standard therapy in the presence and absence of ribavirin, the sustained responders had lower baseline tumor necrosis alpha (TNF-alpha) levels as compared to relapsed responders and non-responders. In patients receiving pegylated therapy, the degree of inflammation as determined by histology was paralleled by high TNF-alpha levels at baseline. In pegylated combination therapy with high dose ribavirin, lower levels of TNF-alpha, transforming growth factor beta (TGF-beta) and fibrosis scores were seen when comparing baseline with follow up. In sustained responders, regardless of therapy, the histological activity scores were lower at follow up as compared to baseline. CONCLUSIONS: Pegylated combination therapy reduces and sustains TNF-alpha levels and liver inflammation as shown by the histological activity index. In addition, it is able to reduce fibrosis as judged both by TGF-beta levels and fibrosis scores as compared to standard therapy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".