Hepatitis C Virus Genotype Distribution in Kermanshah Province, Western Iran
Bibliographic record
Abstract
Six major hepatitis C virus genotypes have been characterized, which vary in their geographical distribution. Knowledge of the distribution of various genotypes is essential for successful future research, treatment and control strategies. In this study, the distribution of HCV genotypes and their association with possible risk factors in a group of HCV infected patients from Kermanshah province of Iran was investigated. HCV viral load test by Real time- PCR method was used for diagnosis of infected cases. The genotypes of cases were revealed using Nested- and Multiplex-PCR and with direct sequencing results were confirmed. Risk factors were also recorded and a multivariate analysis was performed. Among 180 infected people, 138 (76.6%) with 3a genotype, 35 (19.4%) with 1a genotype, 3 (1.7%) with 1b genotype and 4 (2.2%) with 3a and 1b were determined. HCV was transmitted by different routes such as intravenous drug abuse (IVDA), tattooing, sexual, blood transfusion and other risk factors. IVDA and sex are the main risk factors in the men and women, respectively. However, 3a is the predominant genotype in the all groups. This study revealed that 3a is the most prevalent genotypes in Kermanshah province.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".