New Challenges in HIV Research: Combining Phylogenetic Cluster Size and Epidemiological Data
Bibliographic record
Abstract
Abstract An exciting new direction in HIV research is centered on using molecular phylogenetics to understand the social and behavioral drivers of HIV transmission. SPOT was an intervention designed to offer HIV point of care testing to men who have sex with men at a community-based site in Montreal, Canada; at the time of testing, a research questionnaire was also deployed to collect data on socio-demographic and behavioral characteristics of participating men. The men taking part in SPOT could be viewed, from the research perspective, as having been recruited via a convenience sample. Among men who were found to be HIV positive, phylogenetic cluster size was measured using a large cohort of HIV-positive individuals in the province of Quebec. The cluster size is likely subject to under-estimation. In this paper, we use SPOT data to evaluate the association between HIV transmission cluster size and the number of sex partners for MSM, after adjusting for the SPOT sampling scheme and correcting for measurement error in cluster size by leveraging external data sources. The sampling weights for SPOT participants were calculated from another study of men who have sex with men in Montreal by fitting a weight-adjusted model, whereas measurement error was corrected using the simulation-extrapolation conditional on covariates approach.
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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 | 0.258 | 0.552 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".