Genetic polymorphisms of IL28b gene as predictors of response to dual therapy in genotypes 1 and 4-HCV and HIV/HCV-infected patients.
Bibliographic record
Abstract
We describe the genotypes and allele distribution of interleukin 28B (IL28B) rs12979860 and rs8099917 single nucleotide polymorphisms (SNPs) in hepatitis C virus (HCV) G1-4 infected patients, to assess predictive ability and to determine whether the combined determination of two IL28B SNPs might improve sustained virologic response (SVR) prediction of both in HCV mono- and HIV/HCV co-infected patients. IL28B SNPs were genotyped in 269 patients, 181 mono- and 88 co-infected, treated with pegylated interferon and ribavirin. Data stratified by HCV mono- and HCV/HIV co-infected patients showed that 58% and 31% of the rs12979860CC carriers and 49% and 21% of the rs8099917TT carriers had SVR. IL28B SNPs, HCV mono-infection and HCV RNA load were associated with SVR as independent predictors in the two study groups as a whole. ROC curve analyses in the two populations separately, based on gender, age, baseline HCV RNA load and rs12979860/rs8099917 revealed similar receiver operating characteristics (ROC) areas under the curve values. Combining the determination of IL28B SNPs, rs8099917 genotyping improved the response prediction in rs12979860CT carriers only in mono-infected patients. In the era of direct-acting antiviral agents, adopting SVR baseline predictors to orientate naïve-patient management represents an important issue. A model involving IL28B SNPs appears able to predict SVR in both populations.
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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 | 0.001 |
| 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".