Evidence map›Paper›PMID 42030173›Full record

ArticleJMIR formative research2026

Retrieval-Augmented Generation Versus GPT-4o for Patient-Facing Gynecological Cancer Information: Quality Evaluation.

Stephen Pearson, Mimi Reyburn, Conor Foley, Alison Finch, Suzanne Bench, Timothy Bonnici, Louise Rose

Abstract read
In one paragraph

Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.

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

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

7 authors.

Stephen Pearson *Critical Care, University College London Hospitals NHS Foundation Trust, London, England, United Kingdom.ORCID 0009-0009-6523-9892
Mimi Reyburn *University College London Hospitals NHS Foundation Trust, London, United Kingdom.ORCID 0009-0006-4846-7285
Conor Foley *Anaesthetic Department, Whittington Health NHS Trust, London, England, United Kingdom.ORCID 0009-0007-3348-3626
Alison Finch *University College London Hospitals NHS Foundation Trust, London, United Kingdom.ORCID 0000-0002-9758-1416
Suzanne Bench *School of Nursing and Midwifery, London South Bank University, London, England, United Kingdom.ORCID 0000-0002-4499-2959
Timothy Bonnici *Critical Care, University College London Hospitals NHS Foundation Trust, London, England, United Kingdom.ORCID 0000-0003-3113-1188
Louise Rose *Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College London, King's College London, Strand, London, England, WC2R2LS, United Kingdom, 44 02078365454.ORCID 0000-0003-1700-3972

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: Retrieval-augmented generation improved overall quality scores for patient-facing gynecological cancer information mainly through better source attribution.

Indexed as

Genital Neoplasms, FemaleInformation Storage and RetrievalFemaleGenerative Artificial IntelligenceHumansLarge Language ModelsGPT-4ogynecological neoplasmshealth literacylarge language modelspatient education as topicreadabilityretrieval-augmented generation

Identifiers

PMID42030173
PMCPMC13108574

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.