High Dose Consensus Interferon in Nonresponders to Interferon Alpha-2B and Ribavirin with Chronic Hepatitis C
Bibliographic record
Abstract
OBJECTIVES: Approximately 60% of patients with chronic hepatitis C treated with a combination of interferon (IFN) alpha-2b and ribavirin are nonresponders. The purpose of the present study was to evaluate the efficacy of treatment with high dose consensus IFN (CIFN) (15 microg/day) in nonresponders. METHODS: Patients were administered 15 microg CIFN/day. Treatment was stopped in those whose serum hepatitis C virus (HCV) RNA remained detectable at 12 weeks. Those with undetectable HCV RNA at 12 weeks continued on 15 microg three times per week for a further 36 weeks. RESULTS: Twenty-four patients were recruited; six (25%) withdrew before 12 weeks because of side effects. Of the 18 patients who completed 12 weeks of therapy, nine (38%) had undetectable HCV RNA. Seven of nine patients who were HCV RNA-negative at week 12 completed 48 weeks of treatment and two withdrew because of intolerable side effects. At 48 weeks, HCV RNA remained undetectable in three patients. After six months of follow-up off treatment, two patients (8%) continued with no detectable HCV RNA in their sera. CONCLUSIONS: High dose induction therapy with CIFN 15 microg/day in prior nonresponders to IFN alpha-2b and ribavirin led to loss of detectable HCV RNA in 50% of patients, but this response was only sustained in 8% of patients on completion of 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 | 0.001 | 0.003 |
| 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 | 0.000 | 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".