ArticleAesthetic plastic surgery2025
Man Versus Machine: A Comparative Study of Human and ChatGPT-Generated Abstracts in Plastic Surgery Research.
Article in Aesthetic plastic surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSince its 2022 release, ChatGPT has gained recognition for its potential to expedite time-consuming writing tasks like scientific writing. Well-written scientific abstracts are essential for clear and efficient communication of research findings. This study aims to explore ChatGPT-4's capability to produce well-crafted abstracts.
methodsTen abstract-less plastic surgery articles from PubMed were uploaded to ChatGPT, each with a prompt to generate one abstract. Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease Score (FRES) were calculated for all abstracts. Additionally, three physician evaluators blindly assessed the ten original and ten ChatGPT-generated abstracts using a 5-point Likert scale. Results were compared and analyzed using descriptive statistics with mean and standard deviation (SD).
resultsThe original abstracts averaged an FKGL of 14.1 (SD 2.9) and an FRES of 25.2 (SD 14.2), while ChatGPT-generated abstracts had scores of 15.6 (SD 2.4) and 15.4 (SD 13.1), respectively. Collectively, evaluators identified two-thirds of the ChatGPT abstracts, but preferred the ChatGPT abstracts 90% of the time. On average, the evaluators found the ChatGPT abstracts to be more "well written" (4.23 vs. 3.50, p value < 0.001) and "clear and concise" (4.30 vs. 3.53, p value < 0.001) compared to the original abstracts.
conclusionsDespite a slightly higher reading level, evaluators generally preferred ChatGPT abstracts, which received higher ratings overall. These findings suggest ChatGPT holds promise in expediting the creation of high-quality scientific abstracts, potentially enhancing efficiency in research and scientific writing tasks. However, due to its exploratory nature, this study calls for additional research to validate these promising findings. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.
Indexed as
Identifiers
40229613What OpenQuestion holds
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.