Epidemiology and Genotypes of Hepatitis C Virus: A First Study from Jammu (J & K), India
Bibliographic record
Abstract
Background and objectives: Globally, around 200 million people are infected with hepatitis C virus (HCV).India contributes a big proportion of HCV burden with the prevalence estimated between 0.5 and 1.5%.Northeastern tribal populations and areas of Punjab represent the HCV infection hotspots, while in Western and Southern parts of the country, the prevalence is lower.The distribution of HCV genotypes is highly variable.This study was particularly planned to attain knowledge of the prevalent HCV genotypes in Jammu province of Jammu and Kashmir (J&K) state. Materials and methods:Blood samples of patients attending the Department of Medicine, Government Medical College (GMC), Jammu and Kashmir, India, for HCV testing were subjected to serological test at Department of Microbiology, GMC, Jammu.The serum samples were tested for anti-HCV antibodies by enzyme-linked immunosorbent assay (ELISA) and positive samples were subjected to genotyping. Conclusion:Of the 396 samples tested, 33 (8.33%) were found to be HCV positive and 23 of these were included for genotyping.Genotypes 3 and 1 were detected in this region and this was in accordance with other national studies.There is a need for larger field studies to better understand the HCV epidemiology and identify higher prevalence areas and also the distribution of genotypes of HCV.This, being a maiden study from this region, will shed light to allow apposite choice and target efforts for better management of the disease and reduce the burden of chronic liver disease due to HCV.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".