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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".