World Health Organization/HIVResNet Drug Resistance Laboratory Strategy
Bibliographic record
Abstract
With rapidly increasing access to antiretroviral drugs globally, HIV drug resistance (HIVDR) has become a significant public health issue. This requires a coordinated and collaborative response from country level to international level to assess the extent of HIVDR and the establishment of efficient and evidence-based strategies to minimize its appearance and onward transmission. In parallel with the rollout of universal access to HIV treatment, countries are developing protocols based on the recommendations of the World Health Organization (WHO) to measure, at a population level, both transmitted HIVDR and HIVDR emerging during treatment. The WHO in collaboration with international experts (HIVResNet Laboratory Working Group), has developed a laboratory strategy, which has the overall goal of delivering quality-assured HIV genotypic results on specimens derived from the HIVDR surveys. The results will be used to help control the emergence and spread of drug resistance and to guide decision makers on antiretroviral therapy policy at national, regional and global level. The HIVDR Laboratory Strategy developed by the WHO includes several key aspects: the formation of a global network of national, regional and specialized laboratories accredited to perform HIVDR testing using a common set of WHO standard and performance indicators; recommendations of acceptable methods for collection, handling, shipment and storage of specimens in field conditions; and the provision of laboratory technical support, capacity building and quality assurance for network laboratories. The WHO/HIVResNet HIVDR Laboratory Network has been developed along the lines of other successful laboratory networks coordinated by the WHO. As of August 2007, assessment for accreditation has been conducted in 30 laboratories, covering the WHO's African, South-East Asia, Western Pacific, and the Caribbean Regions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".