Viral load testing in a resource‐limited setting: quality control is critical
Bibliographic record
Abstract
BACKGROUND: World Health Organization guidelines now recommend routine use of viral load testing, where available, for patients receiving antiretroviral treatment (ART). However, its use has not been routinely implemented in many resource-limited settings due to cost, availability and accessibility. Viral load testing is complex, making its application in resource-limited settings challenging. We describe the issues encountered by Médecins Sans Frontières (MSF) when using routine viral load testing in a large HIV programme in sub-Saharan Africa. METHODS: Between October 2005 and August 2006, more than 1200 patients on ART had viral load tests at baseline and at three-month intervals performed by a local reference laboratory that was quality assured by an experienced international institution. Concerns with reliability of results halted testing. The quality control measures instituted with a second laboratory and outcomes of these were documented. RESULTS: In 2005 and 2006, only 178 of 334 (53%) previously ART-naïve patients tested after six to 12 months of treatment had viral loads of less than 1000 copies/mL. Similar MSF programmes elsewhere demonstrated virological suppression rates of more than 85%, and duplicate testing showed unacceptable discordance. Laboratory problems encountered included: disregarded quality control; time delays; requirement for retesting; and duplicate sample variations. Potentially harmful clinical outcomes of inaccurate viral load results include: unnecessary ART regimen changes; unnecessary enhanced adherence counselling after "false failures"; and undetected virological failure. CONCLUSIONS: Viral load testing performed without rigorous quality control carries the risk of erroneous and potentially damaging results. Viral load testing should be utilized only if robust quality assurance has been implemented. Our experience in this and other settings led to the development of a guide for assessing the suitability of a laboratory for viral load testing that can be used to help achieve reliable results.
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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 | 0.201 | 0.292 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".