The effects of HIV-1 subtype and ethnicity on the rate of CD4 cell count decline in patients naive to antiretroviral therapy: a Canadian-European collaborative retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Ethnic differences have the potential to confound associations between HIV-1 subtype and immunologic progression. We compared declines in CD4 cell counts during untreated infection for the most prevalent HIV-1 subtypes, focusing on distinguishing between the effects of viral subtype and ethnicity. METHODS: We combined data from 4 European and 6 Canadian cohorts, selecting adults in the stable chronic phase of untreated HIV infection. We estimated the change in square root CD4 cell count over time for subtypes and ethnicities using mixed models, adjusting for covariates selected for their potential effect on initial CD4 cell count or its decline. RESULTS: Data from 9772 patients were analyzed, contributing 79 175 measurements of CD4 cell count and 24 157 person-years of follow-up. Overall, there were no appreciable differences in CD4 cell count decline for viral subtypes A, CRF01_AE, CRF02_AG, C and G compared with viral subtype B; whereas the decline in CD4 cell count in patients of African ancestry was considerably slower than in patients of other ethnicity. When ethnic groups were studied separately, there was evidence for slower declines in CD4 cell count in viral subtypes C, and possibly A and G, compared with viral subtype B in patients of African ancestry but not among patients of other ethnicities, suggesting an interaction between subtype and ethnicity. INTERPRETATION: Ethnicity is a major determinant of CD4 cell count decline; viral subtype differences may have existed but were small compared with the effect of ethnicity and were most apparent in patients of African ancestry. In developing countries, slower CD4 cell count declines among individuals of African descent may translate to a longer asymptomatic phase and increase the opportunity for HIV transmission.
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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 | 0.001 |
| 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) | 0.000 | 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".