Performance of the PointCare NOW System for CD4 Counting in HIV Patients Based on Five Independent Evaluations
Bibliographic record
Abstract
INTRODUCTION: Point-of-care (POC) CD4 testing can improve access to treatment by enabling decentralization and reducing patient loss-to-follow-up. As new POC CD4 technologies become available, their performance should be assessed before widespread deployment. This study reports the findings of five independent evaluations of the PointCare NOW CD4 system. MATERIALS/METHODS: Evaluations were conducted in Southern Africa (Mozambique, South Africa) and North America (Canada, USA). 492 blood samples (55 from HIV-negative blood donors and 437 from HIV-infected patients, including 20 children aged between 12 and 59 months) were tested with both the PointCare NOW and reference flow cytometry instruments. Assessment of bias, precision and levels of clinical misclassification for absolute and percent CD4 count was conducted. RESULTS: PointCare NOW significantly overestimated CD4 absolute counts with a mean relative bias of +35.0%. Bias was greater in samples with CD4 counts below ≤ 350 cells/µl (+51.3%) than in the CD4 >350 cells/µl stratum (15.1%). Bias in CD4% had a similar trend with an overall relative mean bias of +25.6% and a larger bias for low CD4 stratum (+40.2%) than the higher CD4 stratum (+5.8%). Relative bias for CD4% in children was -6.8%. In terms of repeatability, PointCare NOW had a coefficient of variation of 11%. Using a threshold of 350 cells/µl, only 47% of patients who qualified for antiretroviral therapy with reference CD4 testing, would have been eligible for treatment with PointCare NOW test results. This was 39% using a 200 cells/µl threshold. Agreement with infant samples was higher, with 90% qualifying at a 25% eligibility threshold. CONCLUSION: The performance of the PointCare NOW instrument for absolute and percent CD4 enumeration was inadequate for HIV clinical management in adults. In children, the small sample size was not large enough to draw a conclusion. This study also highlights the importance of independent evaluation of new diagnostic technology platforms before deployment.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".