Ribavirin dose management in <scp>HCV</scp> patients receiving ombitasvir/paritaprevir/ritonavir and dasabuvir with ribavirin
Bibliographic record
Abstract
BACKGROUND & AIMS: Some individuals with hepatitis C virus infection treated with direct-acting antivirals require ribavirin to maximize sustained virological response rates. We describe the clinical management of ribavirin dosing in hepatitis C virus-infected patients receiving ombitasvir/paritaprevir/ritonavir and dasabuvir with ribavirin. METHODS: We performed a post hoc analysis of patients receiving ombitasvir/paritaprevir/ritonavir and dasabuvir with ribavirin for 12 or 24 weeks in six phase 3 trials. Multivariate stepwise logistic regression models assessed predictors associated with ribavirin dose adjustments and with developing anaemia. RESULTS: Of 1548 patients, 100 (6.5%) modified ribavirin dose due to haemoglobin declines, of which 99% achieved sustained virological response at 12 weeks post-treatment. Median time to first ribavirin dose reduction was 37 days. Low baseline haemoglobin was significantly associated with an increased risk of requiring ribavirin dose modification (odds ratio: 0.618 [0.518, 0.738]; P < .001) and developing anaemia (odds ratio: 0.379 [0.243, 0.593]; P < .001). CONCLUSIONS: Ribavirin dose reductions were infrequent, occurred early in treatment, and did not impact sustained virological response at 12 weeks post-treatment. Patients with low baseline haemoglobin should be monitored for on-treatment anaemia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".