Mortality of treated HIV-1 positive individuals according to viral subtype in Europe and Canada
Bibliographic record
Abstract
OBJECTIVES: To estimate prognosis by viral subtype in HIV-1-infected individuals from start of antiretroviral therapy (ART) and after viral failure. DESIGN: Collaborative analysis of data from eight European and three Canadian cohorts. METHODS: Adults (N>20 000) who started triple ART between 1996 and 2012 and had data on viral subtype were followed for mortality. We estimated crude and adjusted (for age, sex, regimen, CD4 cell count, and AIDS at baseline, period of starting ART, stratified by cohort, region of origin and risk group) mortality hazard ratios (MHR) by subtype. We estimated MHR subsequent to viral failure defined as two HIV-RNA measurements greater than 500 copies/ml after achieving viral suppression. RESULTS: The most prevalent subtypes were B (15 419; 74%), C (2091; 10%), CRF02AG (1057; 5%), A (873; 4%), CRF01AE (506; 2.4%), G (359; 1.7%), and D (232; 1.1%). Subtypes were strongly patterned by region of origin and risk group. During 104 649 person-years of observation, 1172/20 784 patients died. Compared with subtype B, mortality was higher for subtype A, but similar for all other subtypes. MHR for A versus B were 1.13 (95% confidence interval 0.85,1.50) when stratified by cohort, increased to 1.78 (1.27,2.51) on stratification by region and risk, and attenuated to 1.59 (1.14,2.23) on adjustment for covariates. MHR for A versus B was 2.65 (1.64,4.28) and 0.95 (0.57,1.57) for patients who started ART with CD4 cell count below, or more than, 100 cells/μl, respectively. There was no difference in mortality between subtypes A, B and C after viral failure. CONCLUSION: Patients with subtype A had worse prognosis, an observation which may be confounded by socio-demographic factors.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".