Characterizing HIV Transmission Networks Across the United States
Bibliographic record
Abstract
BACKGROUND: Clinically, human immunodeficiency virus type 1 (HIV-1) pol sequences are used to evaluate for drug resistance. These data can also be used to evaluate transmission networks and help describe factors associated with transmission risk. METHODS: HIV-1 pol sequences from participants at 5 sites in the CFAR Network of Integrated Clinical Systems (CNICS) cohort from 2000-2009 were analyzed for genetic relatedness. Only the first available sequence per participant was included. Inferred transmission networks ("clusters") were defined as ≥2 sequences with ≤1.5% genetic distance. Clusters including ≥3 patients ("networks") were evaluated for clinical and demographic associations. RESULTS: Of 3697 sequences, 24% fell into inferred clusters: 155 clusters of 2 individuals ("dyads"), 54 clusters that included 3-14 individuals ("networks"), and 1 large cluster that included 336 individuals across all study sites. In multivariable analyses, factors associated with being in a cluster included not using antiretroviral (ARV) drugs at time of sampling (P < .001), sequence collected after 2004 (P < .001), CD4 cell count >350 cells/mL (P < .01), and viral load 10,000-100,000 copies/mL (P < .001) or >100,000 copies/mL (P < .001). In networks, women were more likely to cluster with other women (P < .001), and African Americans with other African Americans (P < .001). CONCLUSIONS: Molecular epidemiology can be applied to study HIV transmission networks in geographically and demographically diverse cohorts. Clustering was associated with lack of ARV use and higher viral load, implying transmission may be interrupted by earlier diagnosis and treatment. Observed female and African American networks reinforce the importance of diagnosis and prevention efforts targeted by sex and race.
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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 | 0.005 |
| 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 | 0.001 |
| 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".