Evidence map›Paper›PMID 38832125›Full record

ArticleSexual medicine2024

Evaluation of information accuracy and clarity: ChatGPT responses to the most frequently asked questions about premature ejaculation.

Mehmet Fatih Şahin, Anil Keleş, Rıdvan Özcan, Çağrı Doğan, Erdem Can Topkaç, Murat Akgül, Cenk Murat Yazıci

Abstract read
In one paragraph

Article in Sexual medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 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

7 authors.

Mehmet Fatih ŞahinUrology Department, Tekirdag Namık Kemal University, Süleymanpaşa, Tekirdağ 59020 Turkey.ORCID https://orcid.org/0000-0002-0926-3005
Anil KeleşUrology Department, Tekirdag Namık Kemal University, Süleymanpaşa, Tekirdağ 59020 Turkey.ORCID https://orcid.org/0000-0001-6522-0862
Rıdvan ÖzcanUrology Department, Bursa City Hospital, Nilüfer, Bursa 16110, Turkey.ORCID https://orcid.org/0000-0002-2083-3216
Çağrı DoğanUrology Department, Tekirdag Namık Kemal University, Süleymanpaşa, Tekirdağ 59020 Turkey.ORCID https://orcid.org/0000-0001-9681-2473
Erdem Can TopkaçUrology Department, Tekirdag Namık Kemal University, Süleymanpaşa, Tekirdağ 59020 Turkey.ORCID https://orcid.org/0009-0002-5479-7599
Murat AkgülUrology Department, Tekirdag Namık Kemal University, Süleymanpaşa, Tekirdağ 59020 Turkey.ORCID https://orcid.org/0000-0001-6187-1940
Cenk Murat YazıciUrology Department, Tekirdag Namık Kemal University, Süleymanpaşa, Tekirdağ 59020 Turkey.ORCID https://orcid.org/0000-0001-6140-5181

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Premature ejaculation (PE) is the most prevalent sexual dysfunction in men, and like many diseases and conditions, patients use Internet sources like ChatGPT, which is a popular artificial intelligence-based language model, for queries about this andrological disorder. Aim: The objective of this research was to evaluate the quality, readability, and understanding of texts produced by ChatGPT in response to frequently requested inquiries on PE. Methods: In this study we used Google Trends to identify the most frequently searched phrases related to PE. Subsequently, the discovered keywords were methodically entered into ChatGPT, and the resulting replies were assessed for quality using the Ensuring Quality Information for Patients (EQIP) program. The produced texts were assessed for readability using the Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease Score (FRES), and DISCERN metrics. Outcomes: This investigation has identified substantial concerns about the quality of texts produced by ChatGPT, highlighting severe problems with reading and understanding. Results: The mean EQIP score for the texts was determined to be 45.93 ± 4.34, while the FRES was 15.8 ± 8.73. Additionally, the FKGL score was computed to be 15.68 ± 1.67 and the DISCERN score was 38.1 ± 3.78. The comparatively low average EQIP and DISCERN scores suggest that improvements are required to increase the quality and dependability of the presented information. In addition, the FKGL scores indicate a significant degree of linguistic intricacy, requiring a level of knowledge comparable to about 14 to 15 years of formal schooling in order to understand. The texts about treatment, which are the most frequently searched items, are more difficult to understand compared to other texts about other categories. Clinical Implications: The results of this research suggest that compared to texts on other topics the PE texts produced by ChatGPT exhibit a higher degree of complexity, which exceeds the recommended reading threshold for effective health communication. Currently, ChatGPT is cannot be considered a substitute for comprehensive medical consultations. Strengths and Limitations: This study is to our knowledge the first reported research investigating the quality and comprehensibility of information generated by ChatGPT in relation to frequently requested queries about PE. The main limitation is that the investigation included only the first 25 popular keywords in English. Conclusion: ChatGPT is incapable of replacing the need for thorough medical consultations.

Indexed as

artificial intelligenceChatbotChatGPTpremature ejaculation

Identifiers

PMID38832125
PMCPMC11144523

What OpenQuestion holds

Textmetadata
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.