Characteristics and determinants of <scp>T</scp>‐cell phenotype normalization in <scp>HIV</scp>‐1‐infected individuals receiving long‐term antiretroviral therapy
Bibliographic record
Abstract
OBJECTIVES: Although combination antiretroviral therapy (cART) can restore CD4 T-cell numbers in HIV infection, alterations in T-cell regulation and homeostasis persist. We assessed the incidence and predictors of reversing these alterations with cART. METHODS: ART-naïve adults (n = 4459) followed within the Canadian Observational Cohort and exhibiting an abnormal T-cell phenotype (TCP) prior to cART initiation were studied. Abnormal TCP was defined as having (1) a low CD4 T-cell count (< 532 cells/μL), (2) lost T-cell homeostasis (CD3 < 65% or > 85%) or (3) CD4:CD8 ratio dysregulation (ratio < 1.2). To thoroughly evaluate the TCP, CD4 and CD8 T-cell percentages and absolute counts were also analysed for a median duration of 3.14 years [interquartile range (IQR) 1.48-5.47 years]. Predictors of TCP normalization were assessed using adjusted Cox proportional hazards models. RESULTS: At baseline, 96% of pateints had CD4 depletion, 32% had lost homeostasis and 99% exhibited ratio dysregulation. With treatment, a third of patients had normalized CD4 T-cell counts, but only 85 individuals (2%) had normalized their TCP. In a multivariable model adjusted for age, measurement frequency and baseline regimen, higher baseline CD4 T-cell counts and time-dependent viral suppression independently predicted TCP normalization [hazard ratio (HR) for baseline CD4 T-cell count = 1.42 (1.31-1.54) per 100 cells/μL increase; P ≤ 0.0001; HR for time-dependent suppressed viral load = 3.69 (1.58-8.61); P-value ≤ 0.01]. CONCLUSIONS: Despite effective cART, complete TCP recovery occurred in very few individuals and was associated with baseline CD4 T-cell count and viral load suppression. HIV-induced alterations of the TCP are incompletely reversed by long-term 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".