Evidence map›Paper›PMID 40450565›Full record

ArticleKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2025

Evaluating DeepResearch and DeepThink in anterior cruciate ligament surgery patient education: ChatGPT-4o excels in comprehensiveness, DeepSeek R1 leads in clarity and readability of orthopaedic information.

Onur Gültekin, Jumpei Inoue, Baris Yilmaz, Mehmet Halis Cerci, Bekir Eray Kilinc, Hüsnü Yilmaz, Robert Prill, Mahmut Enes Kayaalp

Registry-linked trialAbstract readComparative Study
In one paragraph

Article in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07631585 (Longitudinal Pre-Post Patient AI Trust Dynamics in Orthopedic Outpatients), which is not on this map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
–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.

NCT07631585 recruitingnot on this mapstarted 2026, after this paper: background citation

Longitudinal Pre-Post Patient AI Trust Dynamics in Orthopedic Outpatients: A Mixed-Methods Observational Study With Matched Physician-Patient Dyads

TypeobservationalSponsorUtku GürhanRan2026 to 2027Enrolled180ConditionsPatient Health Information Seeking Behavior, Trust, Health Literacy, Orthopedics
3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

8 authors.

Onur GültekinDepartment of Orthopaedics and Traumatology, Istanbul Fatih Sultan Mehmet Training and Research Hospital, University of Health Sciences, Istanbul, Turkey.
Jumpei InoueDepartment of Orthopaedic Surgery, Nagoya Tokushukai General Hospital, Kasugai, Aichi, Japan.
Baris YilmazDepartment of Orthopaedics and Traumatology, Istanbul Fatih Sultan Mehmet Training and Research Hospital, University of Health Sciences, Istanbul, Turkey.
Mehmet Halis CerciDepartment of Orthopedics and Traumatology, Memorial Sisli Hospital, Istanbul, Turkey.
Bekir Eray KilincDepartment of Orthopaedics and Traumatology, Istanbul Fatih Sultan Mehmet Training and Research Hospital, University of Health Sciences, Istanbul, Turkey.
Hüsnü YilmazDepartment of Orthopaedics and Traumatology, Istanbul Kartal Dr. Lutfi Kirdar City Hospital, Istanbul, Turkey.
Robert PrillDepartment of Orthopaedics and Traumatology, Brandenburg Medical School Theodor Fontane, University Hospital Brandenburg/Havel, Brandenburg/Havel, Germany.
Mahmut Enes KayaalpDepartment of Orthopaedics and Traumatology, Istanbul Fatih Sultan Mehmet Training and Research Hospital, University of Health Sciences, Istanbul, Turkey.ORCID https://orcid.org/0000-0002-9545-7454

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study compares ChatGPT-4o, equipped with its deep research feature, and DeepSeek R1, equipped with its deepthink feature-both enabling real-time online data access-in generating responses to frequently asked questions (FAQs) about anterior cruciate ligament (ACL) surgery. The aim is to evaluate and compare their performance in terms of accuracy, clarity, completeness, consistency and readibility for evidence-based patient education.

methodsA list of ten FAQs about ACL surgery was compiled after reviewing the Sports Medicine Fellowship Institution's webpages. These questions were posed to ChatGPT and DeepSeek in research-enabled modes. Orthopaedic sports surgeons evaluated the responses for accuracy, clarity, completeness, and consistency using a 4-point Likert scale. Inter-rater reliability of the evaluations was assessed using intraclass correlation coefficients (ICCs). In addition, a readability analysis was conducted using the Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease Score (FRES) metrics via an established online calculator to objectively measure textual complexity. Paired t tests were used to compare the mean scores of the two models for each criterion, with significance set at p < 0.05.

resultsBoth models demonstrated high accuracy (mean scores of 3.9/4) and consistency (4/4). Significant differences were observed in clarity and completeness: ChatGPT provided more comprehensive responses (mean completeness 4.0 vs. 3.2, p < 0.001), while DeepSeek's answers were clearer and more accessible to laypersons (mean clarity 3.9 vs. 3.0, p < 0.001). DeepSeek had lower FKGL (8.9 vs. 14.2, p < 0.001) and higher FRES (61.3 vs. 32.7, p < 0.001), indicating greater ease of reading for a general audience. ICC analysis indicated substantial inter-rater agreement (composite ICC = 0.80).

conclusionChatGPT-4o, leveraging its deep research feature, and DeepSeek R1, utilizing its deepthink feature, both deliver high-quality, accurate information for ACL surgery patient education. While ChatGPT excels in comprehensiveness, DeepSeek outperforms in clarity and readability, suggesting that integrating the strengths of both models could optimize patient education outcomes. LEVEL OF EVIDENCE: Level V.

Indexed as

Anterior Cruciate Ligament InjuriesAnterior Cruciate Ligament ReconstructionComprehensionPatient Education as TopicGenerative Artificial IntelligenceHumansInternetReproducibility of Resultsanterior cruciate ligament (ACL) surgeryartificial intelligence (AI)ChatGPTdeep researchpatient education

Identifiers

PMID40450565
PMCPMC12310095

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.