Evidence map›Paper›PMID 41333499›Full record

ArticleCureus2025

Readability of Professional Medical Content on Acute Appendicitis: A Comparative Cross-Sectional Study of ChatGPT and UpToDate.

Maira Jalil, Saow Renn Ding, Nikita M Talpallikar, Ishita Bhatnagar, Shree V Umashankar, Jocelyn N Wensel

Abstract read
In one paragraph

Article in Cureus, 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.

  1. 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

6 authors.

Maira JalilInternal Medicine, University of Debrecen, Faculty of Medicine, Debrecen, HUN.
Saow Renn DingInternal Medicine, St. George's University School of Medicine, University Centre Grenada, St. George's, GRD.
Nikita M TalpallikarInternal Medicine, D.Y. Patil Medical College, Kolhapur, IND.
Ishita BhatnagarInternal Medicine, Gujarat Medical Education and Research Society (GMERS) Medical College and Hospital, Himatnagar, IND.
Shree V UmashankarInternal Medicine, St. Martinus University, Faculty of Medicine, Willemstad, CUW.
Jocelyn N WenselGeneral Medicine, International University of the Health Sciences, Basseterre, KNA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction Acute appendicitis is one of the most common diseases occurring due to inflammation of the vermiform appendix, which requires surgical intervention. With the advent of standardized artificial intelligence (AI) tools such as ChatGPT (OpenAI, San Francisco, CA), AI-based search engines have emerged as a secondary means for patients to educate themselves about their health. The readability of each response is important for concept understanding as well as the impact of novel therapeutics. Aims This study aims to evaluate and compare the readability of medical information on acute appendicitis generated by an AI language model and UpToDate (Wolters Kluwer Health, Waltham, MA) using established readability metrics. Methodology A comparative cross-sectional study was conducted to evaluate the readability of six ChatGPT-4o and six UpToDate responses on acute appendicitis. Readability parameters were assessed using WebFX (WebFX®, Harrisburg, PA), and differences between sources were analyzed using the Mann-Whitney U test in IBM SPSS Statistics software, version 25 (IBM Corp., Armonk, NY) and R (v4.3.2, The R Core Team, R Foundation for Statistical Computing, Vienna, Austria). Results UpToDate had a higher word count and higher words per sentence than ChatGPT (both p < 0.05). ChatGPT had a lower absolute difficult-word count (p = 0.002) but a higher difficult-word percentage (p = 0.002). Differences in Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), and sentence count were not statistically significant (all

Indexed as

acute appendicitischatgptcomplex wordsgrade levelreadabilitysmog index

Identifiers

PMID41333499
PMCPMC12667559

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.