ArticleAIDS (London, England)2021
Power of Big Data in ending HIV.
Article in AIDS (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Enhanced language models for predicting and understanding HIV care disengagement: a case study in Tanzania.NPJ digital medicine · 2026Article
- Leveraging Electronic Health Records and Claims Data to Improve HIV and Comorbidity Care Trajectories: A Scoping Review.Current HIV/AIDS reports · 2025Article
- Article
- Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response.Current HIV/AIDS reports · 2024Review
- The deubiquitinating enzyme USP44 suppresses hepatocellular carcinoma progression by inhibiting Hedgehog signaling and PDL1 expression.Cell death & disease · 2023Article
- Predicting Treatment Interruption Among People Living With HIV in Nigeria: Machine Learning Approach.JMIR AI · 2023Article
- Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania.PLOS global public health · 2022Article
- Emergence and evolution of big data science in HIV research: Bibliometric analysis of federally sponsored studies 2000-2019.International journal of medical informatics · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The articles in this special issue of AIDS focus on the application of the so-called Big Data science (BDS) as applied to a variety of HIV-applied research questions in the sphere of health services and epidemiology. Recent advances in technology means that a critical mass of HIV-related health data with actionable intelligence is available for optimizing health outcomes, improving and informing surveillance. Data science will play a key but complementary role in supporting current efforts in prevention, diagnosis, treatment, and response needed to end the HIV epidemic. This collection provides a glimpse of the promise inherent in leveraging the digital age and improved methods in Big Data science to reimagine HIV treatment and prevention in a digital age.
Indexed as
Identifiers
What OpenQuestion holds
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.