Progress of implementation of the World Health Organization strategy for HIV drug resistance control in Latin America and the Caribbean
Bibliographic record
Abstract
By the end of 2010, Latin America and the Caribbean (LAC) achieved 63% antiretroviral treatment (ART) coverage. Measures to control HIV drug resistance (HIVDR) at the country level are recommended to maximize the efficacy and sustainability of ART programs. Since 2006, the Pan American Health Organization has supported implementation of the World Health Organization (WHO) strategy for HIVDR prevention and assessment through regional capacity-building activities and direct technical cooperation in 30 LAC countries. By 2010, 85 sites in 19 countries reported early warning indicators, providing information about the extent of potential drivers of drug resistance at the ART site. In 2009, 41.9% of sites did not achieve the WHO target of 100% appropriate first-line prescriptions; 6.3% still experienced high rates (> 20%) of loss to follow-up, and 16.2% had low retention of patients (< 70%) on first-line prescriptions in the first year of treatment. Stock-outs of antiretroviral drugs occurred at 22.7% of sites. Haiti, Guyana, and the Mesoamerican region are planning and implementing WHO HIVDR monitoring surveys or threshold surveys. New HIVDR surveillance tools for concentrated epidemics would promote further scale-up. Extending the WHO HIVDR lab network in Latin America is key to strengthening regional lab capacity to support quality assured HIVDR surveillance. The WHO HIVDR control strategy is feasible and can be rolled out in LAC. Integrating HIVDR activities in national HIV care and treatment plans is key to ensuring the sustainability of this strategy.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".