Determining eligibility for antiretroviral therapy in resource-limited settings using total lymphocyte counts, hemoglobin and body mass index.
Bibliographic record
Abstract
BACKGROUND: CD4+ T lymphocyte (CD4) cell count testing is the standard method for determining eligibility for antiretroviral therapy (ART), but is not widely available in sub-Saharan Africa. Total lymphocyte counts (TLCs) have not proven sufficiently accurate in identifying subjects with low CD4 counts. We developed clinical algorithms using TLCs, hemoglobin (Hb), and body mass index (BMI) to identify patients who require ART. METHODS: We conducted a cross-sectional study of HIV-infected adults in Uganda, who presented for assessment for ART-eligibility with WHO clinical stages I, II or III. Two by two tables were constructed to examine TLC thresholds, which maximized sensitivity for CD4 cell counts <or= 200 cells microL, while minimizing the number offered ART with counts > 350 cells microL. Hb and BMI values were then examined to try to improve model performance. RESULTS: 1787 subjects were available for analysis. Median CD4 cell counts and TLCs, were 239 cells/microL and 1830 cells/microL, respectively. Offering ART to all subjects with a TLCs <or= 2250 cells/microL produced a sensitivity of 0.88 and a false positive ratio of 0.21. Algorithms that treated all patients with a TLC <2000 cells/microL, excluded all patients with a TLC >3000 cells/microL, and used Hb and/or BMI values to determine eligibility for those with TLC values between 2000 and 3000 cells/microL, marginally improved accuracy. CONCLUSION: TLCs appear useful in predicting who would be eligible for ART based on CD4 cell count criteria. Hb and BMI values may be useful in prioritizing patients for ART, but did not improve model accuracy.
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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 | 0.001 | 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".