Mortality by baseline CD4 cell count among HIV patients initiating antiretroviral therapy: evidence from a large cohort in Uganda
Bibliographic record
Abstract
OBJECTIVE: Evaluations of CD4 cell count and other prognostic factors on the survival of HIV patients in sub-Saharan Africa are extremely limited. Funders have been reticent to recommend earlier initiation of treatment. We aimed to examine the effect of baseline CD4 cell count on mortality using data from HIV patients receiving combination antiretroviral therapy (cART) in Uganda. DESIGN: Observational study of patients aged at least 14 years enrolled in 10 clinics across Uganda for which The AIDS Support Organization (TASO) has data. METHODS: CD4 cell count was stratified into categories (<50, 50-99, 100-149, 150-199, 200-249, 250-299, ≥300 cells/μl) and Cox proportional hazards regression was used to model the associations between CD4 cell count and mortality. RESULTS: A total of 22 315 patients were included. 1498 patients died during follow-up (6.7%) and 1433 (6.4%) of patients were lost to follow-up. Crude mortality rates (CMRs) ranged from 53.8 per 1000 patient-years [95% confidence interval (CI) 48.8-58.8] among those with CD4 cell counts of less than 50, to 15.7, (95% CI 12.1-19.3) among those with at least 300 cells/μl. Relative to a baseline CD4 cell count of less than 50 cells/μl, the risk of mortality was 0.75 (95% CI 0.65-0.88), 0.60 (95% CI 0.51-0.70), 0.43 (0.37-0.50), and 0.41 (0.33-0.51) for those with baseline CD4 cell counts of 50-99, 100-149, 150-249, and ≥250 cells/μl, respectively. CONCLUSION: Earlier initiation of cART is associated with increased survival benefits over deferred treatment.
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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) | 0.002 | 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 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".