CD4 <sup>+</sup> T-cell recovery after initiation of antiretroviral therapy in a resource-limited setting: A prospective cohort analysis
Bibliographic record
Abstract
BACKGROUND: CD4(+) T-cell count recovery after antiretroviral therapy (ART) initiation is associated with improved health outcomes. It is unknown how the CD4(+) T-cell counts of African HIV patients recover following ART initiation. METHODS: We examined CD4(+) T-cell count recovery in a large cohort of HIV-positive patients initiating ART in Uganda between 2004 and 2011. We categorized patients according to their CD4(+) T-cell count at ART initiation. All patients received CD4(+) T-cell count evaluations on a biannual basis. We used quantile regression to model the recovery of CD4(+) T-cells during ART. RESULTS: A total of 5,271 patients aged ≥14 years at baseline were included. The median number of CD4(+) T-cell count measurements was 6 (IQR 4-8), and vital status at censoring was known in 97.2% of individuals. Most CD4(+) T-cell count recovery occurred within the first 12 months, with marginal increases beyond 18 months and stabilization after 5 years. The strongest predictor of CD4(+) T-cell count recovery was baseline CD4(+) T-cell count. After 5 years on treatment, the median CD4(+) T-cell count was 334 cells/mm(3) for patients initiating ART with <100 cells/mm(3). Only those initiating ART with >200 cells/mm(3) reached a 5-year median >500 cells/mm(3). Adolescents had the most robust CD4(+) T-cell count recovery with a median increase after 12 months that was 109 cells/mm(3) greater than those initiating ART at age ≥50 years. CONCLUSIONS: In individuals from a resource-limited setting, baseline CD4(+) T-cell count was highly predictive of the maximum CD4(+) T-cell count level achieved while on ART.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".