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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".