Identifying HCV genotype 1 patients at risk of relapse
Bibliographic record
Abstract
OBJECTIVE: The objective of this analysis was to identify predictors of relapse in genotype 1 patients after 48 weeks of treatment with peginterferon plus ribavirin. METHODS: Retrospective analysis of data from treatment-naive genotype 1 patients with an end-of-treatment response after 48 weeks of treatment with peginterferon alpha-2a plus ribavirin 1000/1200 mg/day in the Canadian Pegasys Expanded Access Program. RESULTS: Among treatment-naive genotype 1 patients with an end-of-treatment response (n = 432), the sustained virological response status was known for 405 individuals (sustained virological response n = 328, 81%; relapse n = 77, 19%). Early virological response rates at week 12 were similar in relapsers (98.7%) and sustained responders (98.5%). More relapsers (12 of 77, 15.6%) than sustained responders (15 of 328, 4.6%) had quantifiable hepatitis C virus (HCV) RNA (>or=600 IU/ml) at week 12 and, among these patients, mean and maximum HCV RNA levels were higher in relapsers, although the median values were similar. Factors significantly associated with relapse in the multiple logistic regression analysis include older age (odds ratio: 1.48 per decade, 95% confidence interval: 1.06-2.07; P = 0.023), Caucasian ethnicity (odds ratio: 3.23, confidence interval: 1.25-8.33; P = 0.016), higher baseline serum HCV RNA level (P = 0.005), the drop in HCV RNA between baseline and week 12 (P = 0.026), and the interaction between baseline HCV RNA level and the decrease in HCV RNA between baseline and week 12 (P = 0.032). CONCLUSION: Older age, Caucasian ethnicity, and high baseline HCV RNA level, and a smaller decrease in HCV RNA between baseline and week 12 predict a relapse in genotype 1 patients.
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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 | 0.002 |
| 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".