Effect of Baseline HIV Disease Parameters on CD4+ T Cell Recovery After Antiretroviral Therapy Initiation in Kenyan Women
Bibliographic record
Abstract
BACKGROUND: Antiretroviral therapy (ART) for HIV infection reconstitutes the immune system and improves survival. However, the rate and extent of CD4+ T cell recovery varies widely. We assessed the impact of several factors on immune reconstitution in a large Kenyan cohort. METHODOLOGY/PRINCIPAL FINDINGS: HIV-infected female sex workers from a longitudinal cohort, with at least 1 year of pre-ART and 6 months of post-ART follow-up (n = 79), were enrolled in the current study. The median pre-ART follow-up was 4,040 days. CD4 counts were measured biannually and viral loads where available. The median CD4 count at ART initiation was 180 cells/ul, which increased to 339 cells/ul at the most recent study visit. The rate of CD4+ T cell increase on ART was 7.91 cells/month (mean = 13, range -25.92 to 169.4). LTNP status prior to ART initiation did not associate with the rate of CD4 recovery on ART. In univariate analyses, associations were observed for CD4 recovery rate and duration of pre-ART immunosuppression (r = -0.326, p = 0.004) and CD4 nadir (r = 0.284, p = 0.012). In multivariate analysis including age, CD4 nadir, duration of HIV infection, duration of pre-ART immunosuppression, and baseline viral load, only CD4 nadir (p = 0.007) and not duration of immunosuppression (p = 0.87) remained significantly associated with the rate of CD4 recovery. CONCLUSIONS/SIGNIFICANCE: These data suggest that prior duration of immune suppression does not predict subsequent recovery once ART is initiated and confirm the previous observation that the degree of CD4 depletion prior to ART initiation is the most important determinant of subsequent immune reconstitution.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".