Evidence map›Paper›PMID 41120814›Full record

ArticleJournal of the American Heart Association2025

Use of a Generative Pretrained Transformer to Answer Questions and Facilitate a Large Randomized Controlled Trial.

Devin L Brown, Kayla Novitski, Joelle B Sickler, Rebecca M Hankla, Faria Khattak, Emily Philpot, Valerie Durkalski-Mauldin, Sima Sayyahmelli, Christine Patino, Flora Arevalo-Bermudez and 3 more

Registry-linked trialAbstract read
In one paragraph

Article in Journal of the American Heart Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03812653 (Sleep for Stroke Management and Recovery Trial), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

NCT03812653 naactive not recruitingnot on this map

Sleep for Stroke Management and Recovery Trial

TypeinterventionalSponsorUniversity of MichiganRan2019 to 2026Enrolled3,062ConditionsIschemic Stroke, Sleep Apnea, Sleep Apnea, Obstructive, StrokeArmsCPAP
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Devin L BrownStroke Program University of Michigan Ann Arbor MI USA.ORCID 0000-0002-9815-3421
Kayla NovitskiStroke Program University of Michigan Ann Arbor MI USA.
Joelle B SicklerDepartment of Neurology and Rehabilitation Medicine University of Cincinnati Cincinnati OH USA.
Rebecca M HanklaNox Health Alpharetta GA USA.
Faria KhattakThe Data Coordination Unit (DCU), Department of Public Health Sciences, Medical University of South Carolina Charleston SC USA.
Emily PhilpotThe Data Coordination Unit (DCU), Department of Public Health Sciences, Medical University of South Carolina Charleston SC USA.
Valerie Durkalski-MauldinThe Data Coordination Unit (DCU), Department of Public Health Sciences, Medical University of South Carolina Charleston SC USA.
Sima SayyahmelliDepartment of Neurological Surgery, School of Medicine and Public Health University of Wisconsin-Madison Madison WI USA.ORCID 0000-0001-8745-6497
Christine PatinoAtrium Health Carolinas Rehabilitation Charlotte NC USA.
Flora Arevalo-BermudezIntercoastal Medical Group Sarasota FL USA.
Mitzi RobertsThe University of Alabama at Birmingham Birmingham AL USA.
Allegra SahelianDepartment of Neurosurgery Dignity Health Chandler Regional Medical Center Chandler AZ USA.
Ronald D ChervinSleep Disorders Center and Department of Neurology University of Michigan Ann Arbor MI USA.ORCID 0000-0002-8227-5133

Funding

Sleep for Stroke Management And Recovery Trial (Sleep SMART)U01NS099043 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DEVIN L BROWN, RONALD D CHERVIN · 2018 to 2026
$31.7M
NINDS NIH HHS U01 NS099043
6 · The paper itself

Abstract

backgroundGenerative artificial intelligence may help facilitate clinical trials. We sought to determine whether a customized generative pretrained transformer (GPT) could assist clinical trial sites within a randomized, controlled trial with rapid responses to protocol and procedure-related questions.

methodsWithin a large clinical trial, Sleep SMART (Sleep for Stroke Management and Recovery Trial), we developed, tested, and implemented a customized GPT designed to answer, in real-time, procedure-related questions. This support was offered to all active trial sites and questions and responses were monitored by the central study team. An anonymous survey also queried primary study coordinators about their experiences with the GPT.

resultsOf the 785 questions entered during a 10-month implementation period, 75% were able to be answered per the GPT. On manual review, of the 588 that the GPT reportedly answered, only 13 (2%) were not helpful responses, 12 (2%) were incomplete responses, 5 (1%) were misleading, and 3 (1%) contained the correct response but also provided some conflicting messaging. Of 95 primary study coordinators to whom the survey was sent, 45 (47%) responded. Of those, 21 (47%) reported having used the GPT. Of the 19 who provided more detailed information, 89% found it to be very helpful (n=11) or helpful (n=6). Most (79%) found the responses to be accurate (n=15) or partially accurate (n=2), and 89% were very satisfied (n=10) or satisfied (n=7) with the GPT.

conclusionsThis novel use of a customized GPT suggests it could be valuable in support of clinical sites within a large trial. Further research should confirm equivalent accuracy and safety through a direct comparison to human support. REGISTRATION: URL: https://clinicaltrials.gov/; Unique Identifier: NCT03812653.

Indexed as

Generative Artificial IntelligenceRandomized Controlled Trials as TopicResearch DesignStrokeHumansMulticenter Studies as TopicSurveys and Questionnairesclinical trialsgenerative artificial intelligencenatural language processing

Identifiers

PMID41120814
PMCPMC12684590

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.