Evidence map›Paper›PMID 41565873›Full record

ArticleNPJ digital medicine2026

Enhanced language models for predicting and understanding HIV care disengagement: a case study in Tanzania.

Waverly Wei, Junzhe Shao, Rita Qiuran Lyu, Rebecca Hemono, Xinwei Ma, Joseph Giorgio, Zeyu Zheng, Feng Ji, Xiaoya Zhang, Emmanuel Katabaro and 7 more

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Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Waverly Wei *Marshall School of Business, University of Southern California, Los Angeles, CA, USA.
Junzhe Shao *School of Public Health, University of California, Berkeley, CA, USA.
Rita Qiuran Lyu *School of Public Health, University of California, Berkeley, CA, USA.
Rebecca HemonoSchool of Public Health, University of California, Berkeley, CA, USA.
Xinwei MaDepartment of Economics, University of California, San Diego, CA, USA.
Joseph GiorgioHelen Wills Neuroscience Institute, University of California, Berkeley, CA, USA.
Zeyu ZhengCollege of Engineering, University of California, Berkeley, CA, USA.
Feng JiDepartment of Applied Psychology, University of Toronto, Toronto, ON, Canada.
Xiaoya ZhangDepartment of Family, Youth and Community Sciences, University of Florida, Gainesville, FL, USA.
Emmanuel KatabaroHealth for a Prosperous Nation, Dar es Salaam, Tanzania.
Matilda MloweHealth for a Prosperous Nation, Dar es Salaam, Tanzania.
Amon SabasabaHealth for a Prosperous Nation, Dar es Salaam, Tanzania.
Caroline ListerHealth for a Prosperous Nation, Dar es Salaam, Tanzania.
Siraji ShabaniMinistry of Health, Dodoma, Tanzania.
Prosper NjauMinistry of Health, Dodoma, Tanzania.
Sandra I McCoySchool of Public Health, University of California, Berkeley, CA, USA.
Jingshen WangSchool of Public Health, University of California, Berkeley, CA, USA. jingshenwang@berkeley.edu.

Funding

NIH HHS NIH NIMH 1R01MH125746.
6 · The paper itself

Abstract

Sustained engagement in HIV care and adherence to ART are crucial for meeting the UNAIDS "95-95-95" targets. Disengagement from care remains a significant issue, especially in sub-Saharan Africa. Traditional machine learning (ML) models have had moderate success in predicting disengagement, enabling early intervention. We developed an enhanced large language model (LLM) fine-tuned with electronic medical records (EMRs) to predict individuals at risk of disengaging from HIV care in Tanzania. Using 4.8 million EMR records from the National HIV Care and Treatment Program (2018-2023), we identified risks of ART non-adherence, non-suppressed viral load, and loss to follow-up. Our enhanced LLM may outperform traditional machine learning models and zero-shot LLMs. HIV physicians in Tanzania evaluated the model's predictions and justifications, finding 65% alignment with expert assessments, and 92.3% of the aligned cases were considered clinically relevant. This model can support data-driven decisions and may improve patient outcomes and reduce HIV transmission.

Identifiers

PMID41565873
PMCPMC12909982

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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.