Optimizing outcomes in patients with hepatitis C virus genotype 1 or 4
Why this work is in the frame
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Bibliographic record
Abstract
Currently, many decisions for the treatment of hepatitis C virus (HCV) are based on genotype, which is the most significant baseline predictor of response to therapy; however, it has become increasingly apparent that fixed treatment durations might not be appropriate for all patients. The use of on-treatment predictors such as rapid virological response (RVR) at week 4 and early virological response (EVR) at week 12 can be used to predict the likelihood of achieving a sustained virological response (SVR), helping to tailor treatment to the individual. Until now, EVR has been defined as achieving either undetectable HCV RNA (< 50 IU/ml) or a > 2 log drop in HCV RNA, but still detectable, at week 12. However, rates of SVR in patients achieving an EVR are heterogeneous. It has recently been suggested that by subdividing EVR into RVR (< 50 IU/ml at week 4), complete EVR (HCV RNA < 50 IU/ml at week 12) or partial EVR (HCV RNA > 2 log drop in HCV RNA but still detectable [> 50 IU/ml] at week 12), it might be possible to further improve the prediction of patients likely to achieve an SVR and may allow for tailoring of treatment duration. Genotype 1 and 4 patients achieving an RVR have high rates of SVR and may be candidates for shorter treatment duration. Patients with a complete EVR achieve high SVR rates with the current treatment duration of 48 weeks, whereas patients achieving a partial EVR have lower rates of SVR and could benefit from treatment intensification to 72 weeks. Here, we discuss the importance of baseline predictors of response and the emerging concept of response-guided therapy in genotype 1 and 4 patients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it