Increasing HIV Subtype Diversity and Its Clinical Implications in a Sentinel North American Population
Bibliographic record
Abstract
BACKGROUND: HIV-1 is a highly diverse virus; subtypes may exhibit differences in rates of transmission, disease progression, neurotoxicity, antiretroviral treatment failure profiles and accuracy of viral load measurements. To date, the HIV epidemic in Canada and the rest of the developed world has been largely due to subtype B; however, shifts in subtype epidemiology could have significant implications. OBJECTIVE: To determine whether there has been an increase in HIV subtype diversity in southern Alberta, Canada. METHODS: All 2358 patients receiving any HIV care between December 31, 2001 and December 31, 2010 were included in a retrospective analysis of subtype prevalence and incidence. In an indexed analysis, subtype trends from 1994 to 2010 were also evaluated. RESULTS: Between 2001 and 2010, the prevalence of non-B HIV subtypes in patients with a known subtype increased from 7% to 24%. In 2010, the most prevalent non-B subtypes were C (65%), A (11%), CRF02_AG (9.7%), CRF01_AE (4.9%), D (3.9%), G (2.9%) and CRF06_cpx (1.5%). In the indexed analysis, there was an overall proportional increase in non-B subtypes of 2.3% per year. The year-over-year increase in the prevalence of patients infected with a nonsubtype B virus increased from 13% from 1995 to 2002 to 27% from 2003 to 2010 (P=0.01). Incident non-B subtype cases increased from 9.6% to 32.4% over these time periods. CONCLUSIONS: This recent and dramatic shift in HIV strain diversity in Canada is unprecedented and may have important public health, research and clinical consequences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".