HIV-1 Transmission Patterns in Antiretroviral Therapy-Naıve, HIV-Infected North Americans Based on Phylogenetic Analysis by Population Level and Ultra-Deep DNA Sequencing
Bibliographic record
Abstract
Factors that contribute to the transmission of human immunodeficiency virus type 1 (HIV-1), especially drug-resistant HIV-1 variants remain a significant public health concern. In-depth phylogenetic analyses of viral sequences obtained in the screening phase from antiretroviral-naı̈ve HIV-infected patients seeking enrollment in EPZ108859, a large open-label study in the USA, Canada and Puerto Rico (ClinicalTrials.gov NCT00440947) were examined for insights into the roles of drug resistance and epidemiological factors that could impact disease dissemination. Viral transmission clusters (VTCs) were initially predicted from a phylogenetic analysis of population level HIV-1 pol sequences obtained from 690 antiretroviral-naı̈ve subjects in 2007. Subsequently, the predicted VTCs were tested for robustness by ultra deep sequencing (UDS) using pyrosequencing technology and further phylogenetic analyses. The demographic characteristics of clustered and non-clustered subjects were then compared. From 690 subjects, 69 were assigned to 1 of 30 VTCs, each containing 2 to 5 subjects. Race composition of VTCs were significantly more likely to be white (72 % vs. 60%; p = 0.04). VTCs had fewer reverse transcriptase and major PI resistance mutations (9 % vs. 24%; p = 0.002) than non-clustered sequences. Both men-who-have-sex-with-men (MSM) (68 % vs. 48%; p = 0.001) and Canadians (29 % vs. 14%; p = 0.03) were significantly more frequent in
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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 | 0.000 | 0.001 |
| 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 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".