Evaluation of a Low-Cost Method, the Guava EasyCD4 Assay, to Enumerate CD4-Positive Lymphocyte Counts in HIV-Infected Patients in the United States and Uganda
Bibliographic record
Abstract
OBJECTIVE: To evaluate the EasyCD4 assay, a less expensive method to enumerate CD4+ lymphocytes, in resource-limited settings. DESIGN: Cross-sectional study conducted in the United States and Uganda. METHODS: We compared CD4+ cell counts obtained on replicate samples from HIV-infected patients by the EasyCD4 assay, a microcapillary flow-based system, and by standard flow cytometry or FACSCount with linear regression and the Bland-Altman method. RESULTS: Two hundred eighteen samples were analyzed (77 in the United States and 141 in Uganda). In the United States, mean +/- SD CD4 was 697 +/- 438 cells/microL by standard flow cytometry and 688 +/- 451 cells/microL by EasyCD4. In Uganda, the mean +/- SD CD4 was 335 +/- 331 cells/microL by FACSCount and 340 +/- 327 cells/microL by EasyCD4. The 2 methods were highly correlated (US cohort, r2 = 0.97, slope = 1.0, intercept = -18; Ugandan cohort, r2 = 0.92; slope = 0.95; intercept = 23). The mean differences in CD4 cell counts were 9.0 and -4.6 cells/microL for the US and Ugandan cohorts, respectively, and they were not significant in either cohort. In the Ugandan cohort, sensitivity and specificity of the EasyCD4 for CD4 below 200 cells/microL were 90% and 98%, respectively. Positive predictive value was 96%; negative predictive value was 93%. CONCLUSIONS: Our results suggest that EasyCD4 may be used with high positive and negative predictive value in resource-limited settings.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".