Highlights from the 3rd international HIV/viral hepatitis Co-infection meeting - HIV/viral hepatitis: improving diagnosis, antiviral therapy and access
Bibliographic record
Abstract
The International AIDS Society convened the 3rd International HIV/Viral Hepatitis Co-Infection Meeting on 17 July 2016 as part of the pre-conference program preceding the 21st International AIDS Conference held in Durban, South Africa. The meeting brought together a diversity of scientific, technical and community interests to discuss opportunities and challenges for increased prevention, diagnosis and treatment of viral hepatitis in people living with HIV, particularly in low- and middle-income settings. The objectives of the meeting were:i.To review the latest therapeutic developments in viral hepatitis;ii.To identify challenges such as high cost of medications for hepatitis C virus (HCV) and risk of developing viral resistance, and successes, such as the provision of HCV treatment in community-based settings, movements to reduce drug costs and increasing access, in relation to scaling up diagnosis, screening, antiviral treatment and prevention of viral hepatitis;iii.To advance the agenda for elimination of viral hepatitis as a public health problem. Discussions centred around the six key interventions outlined by the World Health Organization Global Health Sector Strategy on Viral Hepatitis 2016-2021: hepatitis B virus (HBV) vaccination (including birth dose); safe injection practices plus safe blood; harm reduction among people who inject drugs; safer sex practices; hepatitis B treatment; and hepatitis C cure. This article summarizes the main issues and findings discussed during the pre-conference meeting. One of the recommendations from the meeting delegates is universal implementation of birth dose vaccination for HBV without further delay to prevent mother-to-child transmission of infection. There is also the need to implement screening and treatment of hepatitis among pregnant women. A call was made for concerted efforts to be put together by all stakeholders towards addressing some of the structural barriers, including criminalization of drug use, discrimination and stigma that people living with viral hepatitis face. Finally, the need for greater advocacy was highlighted to enable access to therapy of viral hepatitis at lower cost than currently prevails. Implementation of these resolutions will help in achieving the target of eliminating viral hepatitis as a public health threat.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".