Worldwide prevalence, genotype distribution and management of hepatitis C
Bibliographic record
Abstract
Hepatitis C virus (HCV) is one of the leading causes of chronic liver disease, cirrhosis, and hepatocellular carcinoma, resulting in major global public health concerns. The HCV infection is unevenly distributed worldwide, with variations in prevalence across and within countries. The studies on molecular epidemiology conducted in several countries provide an essential supplement for a comprehensive knowledge of HCV epidemiology, genotypes, and subtypes, along with providing information on the impact of current and earlier migratory flows. HCV is phylogenetically classified into 8 major genotypes and 57 subtypes. HCV genotype and subtype distribution differ according to geographic origin and transmission risk category. Unless people with HCV infection are detected and treated appropriately, the number of deaths due to the disease will continue to increase. In 2015, 1.75 million new viral infections were mostly due to unsafe healthcare procedures and drug use injections. In the same year, access to direct-acting antivirals was challenging and varied in developing and developed countries, affecting HCV cure rates based on their availability. The World Health Assembly, in 2016, approved a global strategy to achieve the elimination of the HCV public health threat by 2030 (by reducing new infections by 90% and deaths by 65%). Globally, countries are implementing policies and measures to eliminate HCV risk based on their distribution of genotypes and prevalence.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".