Evaluation of Dynabeads and Cytospheres Compared With Flow Cytometry to Enumerate CD4+ T Cells in HIV-Infected Ugandans on Antiretroviral Therapy
Bibliographic record
Abstract
BACKGROUND: Laboratory-based monitoring of antiretroviral therapy is essential but adds a significant cost to HIV care. The World Health Organization 2006 guidelines support the use of CD4 lymphocyte count (CD4) to define treatment failure in resource-limited settings. METHODS: We compared CD4 obtained on replicate samples from 497 HIV-positive Ugandans (before and during ART) followed for 18 months by 2 manual bead-based assays, Dynabeads (Dynal Biotech), and Cytospheres (Beckman Coulter) with those generated by flow cytometry at the Infectious Diseases Institute in Kampala, Uganda. RESULTS: We tested 1671 samples (123 before ART) with Dynabeads and 1444 samples (91 before ART) with Cytospheres. Mean CD4 was 231 cells/mm (SD, 139) and 239 cells/mm (SD, 140) by Dynabeads and flow cytometry, respectively. Mean CD4 was 186 cells/mm (SD, 101) and 242 cells/mm (SD, 136) by Cytospheres and flow cytometry, respectively. The mean difference in CD4 count by flow cytometry versus Dynabeads were 8.8 cells/mm (SD, 76.0) and versus Cytospheres were 56.8 cells/mm (SD, 85.8). The limits of agreement were -140.9 to 158.4 cells/mm for Dynabeads and -112.2 to 225.8 cells/mm for Cytospheres. Linear regression analysis showed higher correlation between flow cytometry and Dynabeads (r=0.85, r=0.73, slope=0.85, intercept=28) compared with the correlation between flow cytometry and Cytospheres (r=0.78, r=0.60, slope=0.58, intercept=45). Area under the receiver operating characteristics curve to predict CD4<200 cells/mm was 0.928 for Dynabeads and 0.886 for Cytospheres. CONCLUSION: Although Dynabeads and Cytospheres both underestimated CD4 lymphocyte count compared with flow cytometry, in resource-limited settings with low daily throughput, manual bead-based assays may provide a less expensive alternative to flow cytometry.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".