The World Health Organization's global strategy for prevention and assessment of HIV drug resistance
Bibliographic record
Abstract
Antiretroviral treatment (ART) for HIV is being scaled up rapidly in resource-limited countries. Treatment options are simplified and standardized, generally with one potent first-line regimen and one potent alternate first-line regimen recommended. Widespread HIV drug resistance (HIVDR) was initially feared, but reports from resource-limited countries suggest that initial ART programmes are as effective as in resource-rich countries, which should limit HIV drug resistance if programme effectiveness continues during scale-up. ART interruptions must be minimized to maintain viral suppression on the first-line regimen for as long as possible. Lack of availability of appropriate second-line drugs is a concern, as is the additional accumulation of resistance mutations in the absence of viral load testing to determine failure. The World Health Organization (WHO) recommends a minimum-resource strategy for prevention and assessment of HIVDR in resource-limited countries. The WHO's Global Network HIVResNet provides standardized tools, training, technical assistance, laboratory quality assurance, analysis of results and recommendations for guidelines and public health action. National strategies focus on assessments to guide immediate public health action to improve ART programme effectiveness in minimizing HIVDR and to guide regimen selection. Globally, WHO HIVResNet collects and analyses data to support evidence-based international policies and guidelines. Financial support is provided by major international organizations and technical support from HIVDR experts worldwide. As of December 2007, 25 countries were planning or implementing the strategy; seven countries report results in this supplement.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".