Harnessing the Prevention Benefits of Antiretroviral Therapy to Address HIV and Tuberculosis
Bibliographic record
Abstract
After 30 years we are still struggling to address a devastating HIV pandemic in which over 25 million people have died. In 2010, an estimated 34 million people were living with HIV, around 70% of whom live in sub-Saharan Africa. Furthermore, in 2009 there were an estimated 1.2 million new HIV-associated TB cases, and tuberculosis (TB) accounted for 24% of HIV-related deaths. By the end of 2010, 6.6 million people were taking antiretroviral therapy (ART), around 42% of those in need as defined by the 2010 World Health Organization (WHO) guidelines. Despite this achievement, around 9 million people were eligible and still in need of treatment, and new infections (approximately 2.6 million in 2010 alone) continue to add to the future caseload. This combined with the international fiscal crisis has led to a growing concern regarding weakening of the international commitment to universal access and delivery of the Millennium Development Goals by 2015. The recently launched UNAIDS/WHO Treatment 2.0 platform calls for accelerated simplification of ART, in line with a public health approach, to achieve and sustain universal access to ART, including maximizing the HIV and TB preventive benefit of ART by treating people earlier, in line with WHO 2010 normative guidance. The potential individual and public health prevention benefits of using treatment in the prevention of HIV and TB enhance the value of the universal access pledge from a life-saving initiative, to a strategic investment aimed at ending the HIV epidemic. This review analyzes the gaps and summarizes the evidence regarding ART in the prevention of HIV and TB.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".