Risk factors for difficult‐to‐treat hepatitis C virus genotype 4r in Rwanda and implications for elimination in sub‐Saharan Africa
Bibliographic record
Abstract
In sub-Saharan Africa, there exist distinct HCV genotype (GT) subtypes harbouring resistance-associated substitutions to commonly used non-structural protein 5A (NS5A) inhibitor-based direct-acting antiviral (DAA) regimens. In particular, GT4r subtype has demonstrated high rates of treatment failure. In the absence of routine viral sequencing in sub-Saharan Africa, it is important to identify sociodemographic, epidemiologic, and clinical characteristics that may be associated with GT4r infection. Methods: A secondary analysis was performed on data from 300 adults with HCV GT4 enrolled in a prospective trial assessing the safety and efficacy of sofosbuvir-ledipasvir in Rwanda in 2017. The association between characteristics at enrolment and GT subtype was assessed by chi-square analysis and logistic regression. In multivariate analysis, there were a higher proportion of participants with GT4r subtype with age <40 years (OR: 3.6, 95% CI: 1.3-10.5, p = 0.02), previous hospitalization (OR: 2.5, 95% CI: 1.3-5.0, p = 0.006), previous surgery (OR: 2.2, 95% CI: 1.1-4.2, p = 0.03), cirrhosis (OR: 3.2, 95% CI: 1.3-7.5, p = 0.008) and baseline HCV RNA >1 million IU/ml (OR: 3.4, 95% CI: 1.6-6.9, p = 0.001). Rwandan adults with GT4r are more likely to be younger, have a history of hospital admissions and surgeries and have more active or advanced liver disease compared to those with other GT4 subtypes. In the absence of advanced diagnostics to assess GT subtype, patients with these characteristics may warrant closer monitoring for treatment failure or alternative DAA regimens. More treatment experience with diverse DAA regimens is urgently needed for GT subtypes particular to this region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".