Evidence map›Paper›PMID 39996902›Full record

ArticleCurrent oncology (Toronto, Ont.)2025

Leveraging Large Language Models for High-Quality Lay Summaries: Efficacy of ChatGPT-4 with Custom Prompts in a Consecutive Series of Prostate Cancer Manuscripts.

Emily Rinderknecht, Anna Schmelzer, Anton Kravchuk, Christopher Goßler, Johannes Breyer, Christian Gilfrich, Maximilian Burger, Simon Engelmann, Veronika Saberi, Clemens Kirschner and 7 more

Abstract read
In one paragraph

Article in Current oncology (Toronto, Ont.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

17 authors.

Emily RinderknechtDepartment of Urology, St. Josef Medical Center, University of Regensburg, 93053 Regensburg, Germany.ORCID 0009-0002-7283-5237
Anna SchmelzerDepartment of Urology, St. Elisabeth Hospital Straubing, 94315 Straubing, Germany.
Anton KravchukDepartment of Urology, St. Elisabeth Hospital Straubing, 94315 Straubing, Germany.ORCID 0009-0003-3365-1563
Christopher GoßlerDepartment of Urology, St. Josef Medical Center, University of Regensburg, 93053 Regensburg, Germany.ORCID 0000-0002-6376-7669
Johannes BreyerDepartment of Urology, St. Josef Medical Center, University of Regensburg, 93053 Regensburg, Germany.
Christian GilfrichDepartment of Urology, St. Elisabeth Hospital Straubing, 94315 Straubing, Germany.ORCID 0000-0003-0211-0636
Maximilian BurgerDepartment of Urology, St. Josef Medical Center, University of Regensburg, 93053 Regensburg, Germany.
Simon EngelmannDepartment of Urology, St. Josef Medical Center, University of Regensburg, 93053 Regensburg, Germany.
Veronika SaberiDepartment of Urology, St. Josef Medical Center, University of Regensburg, 93053 Regensburg, Germany.
Clemens KirschnerDepartment of Urology, St. Josef Medical Center, University of Regensburg, 93053 Regensburg, Germany.
Dominik von WinningDepartment of Urology, St. Elisabeth Hospital Straubing, 94315 Straubing, Germany.
Roman MayrDepartment of Urology, St. Josef Medical Center, University of Regensburg, 93053 Regensburg, Germany.ORCID 0000-0003-1796-3260
Christian WülfingWorking Group on Artificial Intelligence and Digitalization of the German Society of Urology.ORCID 0000-0002-0251-2654
Hendrik BorgmannWorking Group on Artificial Intelligence and Digitalization of the German Society of Urology.
Stephan BuseDepartment of Urology, Alfried Krupp Krankenhaus, 45131 Essen, Germany.ORCID 0000-0003-0229-7875
Maximilian HaasDepartment of Urology, St. Josef Medical Center, University of Regensburg, 93053 Regensburg, Germany.ORCID 0000-0003-3132-0130
Matthias MayWorking Group on Artificial Intelligence and Digitalization of the German Society of Urology.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clear and accessible lay summaries are essential for enhancing the public understanding of scientific knowledge. This study aimed to evaluate whether ChatGPT-4 can generate high-quality lay summaries that are both accurate and comprehensible for prostate cancer research in

Indexed as

Prostatic NeoplasmsGenerative Artificial IntelligenceHumansLarge Language ModelsMaleartificial intelligence in healthcarelanguage model applicationslay abstractsmedical applicationpatient communicationplain language summariesprompt designreadability metricsscientific literacytext generation

Identifiers

PMID39996902
PMCPMC11854015

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.