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 peopleinfected with HIV to become aware of their status, access antiretroviral therapies and ultimatelybe 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 decentralisingtesting increases patient access to diagnostics, it presents many challenges with regard totraining and assuring the quality of tests and testing. To ensure synergies, the London Schoolof Hygiene & Tropical Medicine held a series of consultations with countries with an interestin quality assurance and their implementing partners, and agreed on an external qualityassessment (EQA) programme to ensure reliable results so that the results lead to the bestpossible care for HIV patients. As a result of the consultations, EQA International wasestablished, bringing together EQA providers and implementers to develop a strategic planfor countries to establish national POC EQA programmes and to estimate the cost of setting upand maintaining the programme. With the dramatic increase in the number of proficiencytesting panels required for thousands of POC testing sites across Africa, it is important tofacilitate technology transfer from global EQA providers to a network of regional EQA centresin Africa for regional proficiency testing panel production. EQA International will continue toidentify robust and cost-effective EQA technologies for quality POC testing, integrating noveltechnologies to support sustainable country-owned EQA programmes in Africa.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".