Age, Adherence and Injection Drug use Predict Virological Suppression among Men and Women Enrolled in a Population-Based Antiretroviral Drug Treatment Programme
Bibliographic record
Abstract
OBJECTIVES: To characterize 1-year virological response to antiretroviral therapy and its determinants by sex. METHODS: This is a population-based analysis of antiretroviral therapy naive HIV-positive adult men and women. Factors associated with sex and with plasma HIV RNA viral load suppression to below 500 copies/ml were examined using non-parametric tests and logistic regression analyses. RESULTS: A total of 739 subjects (92 women and 647 men) were eligible. Female participants were younger (34 vs 37 years; P < 0.001), less likely to have AIDS (6.5 vs 14.4%; P = 0.039), more frequently injection drug users (44.6 vs 25.2%; P = 0.001) and were less likely to be adherent to therapy (34.8 vs 62.9%; P < 0.001) than male participants. There was no difference in baseline median CD4 count (P = 0.424) or HIV RNA levels (P = 0.140), physician experience (P = 0.057), or with respect to antiretroviral regimens containing protease inhibitors or non-nucleoside reverse transcriptase inhibitors (P = 0.911). With treatment, 46.7% (43/92) of women and 64.8% (419/647) of men (P = 0.001) suppressed HIV RNA viral load to below 500 copies/ml at 1 year. In a multivariate analysis, the association of sex with HIV RNA response to antiretroviral therapy fell from statistical significance (odds ratio 1.18; 95% CI: 0.72-1.95) after adjusting for adherence, injection drug use and age. CONCLUSION: Our data indicate that in this population-based setting, sex differences in 1-year virological response to antiretroviral therapy are explained by age, adherence and injection drug use.
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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 | 0.003 |
| 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 | 0.001 | 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".