Evaluating the Readability of Pediatric Neurocutaneous Syndromes-Related Patient Education Material Created by a Custom GPT With Retrieval Augmentation.

Abstract read

In one paragraph

Article in JMIR dermatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Prompt injection of OpenAI custom GPTs leaks informatics secrets.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
Article

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

2 authors.

Nneka EdeDepartment of Biomedical Engineering, Cockrell School of Engineering, The University of Texas at Austin, 1500 Red River Street, Austin, TX, 78701, United States, +15124955555.ORCID 0009-0003-9831-4736

Robyn OkerekeDepartment of Dermatology, Oregon Health and Sciences University, Portland, OR, United States.ORCID 0009-0005-8770-328X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: In our study, we developed a GPT assistant with a custom knowledge base for neurocutaneous diseases, tested its ability to answer common patient questions, and showed that a GPT using retrieval augmentation generation can improve the readability of patient educational material without being prompted for a specific reading level.

Indexed as

ComprehensionPatient Education as TopicChildHumansanswerAPIapplication programming interfaceartificial intelligenceChatGPTcustom GPTcutaneousdermatologyeducationalgenerative AIGPT assistanthealth educationlarge language modelLLMsmachine learningnatural language processingneurocutaneous syndromesNLPOpenAIpatient educationreadabilityresponseskin

Identifiers

PMID40669085

DOI10.2196/59054

PMCPMC12286582

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.