Characterizing viral subtypes to assess patterns of HIV transmission
Bibliographic record
Abstract
We characterized HIV-1 subtypes among 204 persons newly diagnosed with HIV in Ontario from 2003 to 2005 using samples from the Canadian HIV Strain and Drug Resistance Surveillance Program. We examined HIV-1 subtype by demographic characteristics and exposure category, and determined independent predictors of infection with a non-B HIV subtype using multivariate logistic regression. The distribution of HIV subtypes was: B 77.0%, C 10.3%, AG 4.9%, A 2.5%, AE 2.5% and others 3.0%. Overall, 23.0% were non-B, greater in women than in men (62.8% versus 12.4%, P < 0.0001) and persons under 35 years (31.1% versus 18.5% in those ≥35, P = 0.04). Non-B subtype was predominant (78.9%) among persons from HIV-endemic regions and considerable (28.6%) among other persons infected heterosexually. In multivariate modelling adjusted for gender, non-B subtype was significantly associated with birth in an HIV-endemic region (adjusted odds ratio [aOR] 59.2, P < 0.0001) and heterosexual exposure (aOR 6.3, P = 0.02). Additionally, compared with men who had sex with men, non-B subtype was greater among heterosexual women (aOR 17.8, P < 0.001) and women who injected drugs (injection drug use, aOR 13.4, P = 0.01). We found a non-negligible proportion of non-B subtypes among women infected heterosexually not from HIV-endemic countries, providing interesting insights into HIV transmission patterns.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".