ArticleAIDS and behavior2025
HIV Risk Score and Prediction Model in the United States: A Scoping Review.
Article in AIDS and behavior, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Identify Patients at Risk of HIV Using a Clinical Large Language Model from Electronic Health Records.medRxiv : the preprint server for health sciences · 2026Article
- Identify Patients at Risk of HIV Using a Clinical Large Language Model from Electronic Health Records.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- HIV Prevention Continuum Outcomes Following Implementation of a Municipal HIV Self-Testing Program.AIDS and behavior · 2026Article
- HIV Risk Score and Prediction Model in the United States: A Scoping Review.AIDS and behavior · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Human immunodeficiency virus (HIV) remains a public health issue in the U.S., affecting approximately 1.2 million individuals, many of whom are unaware of their infection status. This study reviews predictors and the performance of HIV risk prediction models. We analyzed 18 studies published since 2010, which featured logistic regression, survival analysis, and machine learning techniques. These studies focused on diverse populations, including men who have sex with men, emergency department visitors, and the general population. Key predictors of HIV risk included demographics (age, sex, race) and behavioral factors (sexual practices, drug use). Electronic health records (EHR) documenting diagnoses of sexually transmitted infection (STI) were significant in all models. Behaviors like condomless sex, multiple sexual partners, and drug use were also strongly linked to increased risk scores. However, we noted a lack of social determinants of health in risk models, and a gap in studies focusing on cis female and transgender populations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.