Early Prediction of Nonresponders to Treatment with Interferon Alpha-2B and Ribavirin in Patients with Chronic Hepatitis C
Bibliographic record
Abstract
BACKGROUND: Treatment of chronic hepatitis C virus (HCV) infection with interferon alpha-2b and ribavirin is costly in terms of side effects, medical resources and drug costs. Furthermore, less than 50% of patients overall have a sustained virological response (SVR). OBJECTIVE: To determine if the log fall in HCV RNA between baseline and week 1 (b-wk1) and between baseline and week 4 (b-wk4) after starting treatment could identify the nonresponders. PATIENTS AND METHODS: Sixty-three patients who had completed a full course of therapy were identified. Quantitative measurements of HCV RNA were analyzed from stored sera, collected prospectively. RESULTS: SVR was achieved in 47.1% and 47.3% of patients in the b-wk1 and b-wk4 groups, respectively. No patients had an SVR with a fall in HCV RNA of less than 0.35 log10 and 1.05 log10 at week 1 and week 4, respectively. This accounted for 44.4% and 51.7% of the nonresponders in the b-wk1 and b-wk4 groups, respectively. Once the decline in viral load was known, genotype, age, sex and baseline viral load did not provide additional power in predicting treatment responses. CONCLUSION: A fall of 1.05 log10 in HCV RNA at week 4 predicts those patients who will not respond, identifying one-half of all nonresponders; this allows therapy to be stopped early, without depriving any patient who would have an SVR from treatment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".