Quality assurance for HIV point-of-care testing and treatment monitoring assays
Bibliographic record
Abstract
In 2015, UNAIDS launched the 90-90-90 targets aimed at increasing the number of people infected with HIV to become aware of their status, access antiretroviral therapies and ultimately be virally suppressed. To achieve these goals, countries may need to scale up point-of-care (POC) testing in addition to strengthening central laboratory services. While decentralising testing increases patient access to diagnostics, it presents many challenges with regard to training and assuring the quality of tests and testing. To ensure synergies, the London School of Hygiene & Tropical Medicine held a series of consultations with countries with an interest in quality assurance and their implementing partners, and agreed on an external quality assessment (EQA) programme to ensure reliable results so that the results lead to the best possible care for HIV patients. As a result of the consultations, EQA International was established, bringing together EQA providers and implementers to develop a strategic plan for countries to establish national POC EQA programmes and to estimate the cost of setting up and maintaining the programme. With the dramatic increase in the number of proficiency testing panels required for thousands of POC testing sites across Africa, it is important to facilitate technology transfer from global EQA providers to a network of regional EQA centres in Africa for regional proficiency testing panel production. EQA International will continue to identify robust and cost-effective EQA technologies for quality POC testing, integrating novel technologies to support sustainable country-owned EQA programmes in Africa.
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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 | 0.113 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".