Evidence map›Paper›PMID 39525124›Full record

ArticleCureus2024

Assessing the Clinical Appropriateness and Practical Utility of ChatGPT as an Educational Resource for Patients Considering Minimally Invasive Spine Surgery.

Advith Sarikonda, Robert Abishek, Emily L Isch, Arbaz A Momin, Mitchell Self, Abhijeet Sambangi, Angeleah Carreras, Jack Jallo, Jim Harrop, Ahilan Sivaganesan

Abstract read
In one paragraph

Article in Cureus, 2024. 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.

  1. Article
  2. Article
  3. Review
  4. 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

10 authors.

Advith SarikondaDepartment of Neurological Surgery, Thomas Jefferson University, Philadelphia, USA.
Robert AbishekDepartment of Neurological Surgery, Thomas Jefferson University, Philadelphia, USA.
Emily L IschDepartment of General Surgery, Division of Plastic Surgery, Thomas Jefferson University Hospital, Philadelphia, USA.
Arbaz A MominDepartment of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, USA.
Mitchell SelfDepartment of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, USA.
Abhijeet SambangiDepartment of Neurological Surgery, Thomas Jefferson University, Philadelphia, USA.
Angeleah CarrerasDepartment of Neurological Surgery, Thomas Jefferson University, Philadelphia, USA.
Jack JalloDepartment of Neurosurgery, Thomas Jefferson Medical College, Philadelphia, USA.
Jim HarropDepartment of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, USA.
Ahilan SivaganesanDepartment of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction Minimally invasive spine surgery (MISS) has evolved over the last three decades as a less invasive alternative to traditional spine surgery, offering benefits such as smaller incisions, faster recovery, and lower complication rates. With patients frequently seeking information about MISS online, the comprehensibility and accuracy of this information are crucial. Recent studies have shown that much of the online material regarding spine surgery exceeds the recommended readability levels, making it difficult for patients to understand. This study explores the clinical appropriateness and readability of responses generated by Chat Generative Pre-Trained Transformer (ChatGPT) to frequently asked questions (FAQs) about MISS. Methods A set of 15 FAQs was formulated based on clinical expertise and existing literature on MISS. Each question was independently inputted into ChatGPT five times, and the generated responses were evaluated by three neurosurgery attendings for clinical appropriateness. Appropriateness was judged based on accuracy, readability, and patient accessibility. Readability was assessed using seven standardized readability tests, including the Flesch-Kincaid Grade Level and Flesch Reading Ease (FRE) scores. Statistical analysis was performed to compare readability scores across preoperative, postoperative, and intraoperative/technical question categories. Results The mean readability scores for preoperative, postoperative, and intraoperative/technical questions were 15±2.8, 16±3, and 15.7±3.2, respectively, significantly exceeding the recommended sixth- to eighth-grade reading level for patient education (p=0.017). Differences in readability across individual questions were also statistically significant (p<0.001). All responses required a reading level above 11th grade, with a majority indicating college-level comprehension. Although preoperative and postoperative questions generally elicited clinically appropriate responses, 50% of intraoperative/technical questions yielded either "inappropriate" or "unreliable" responses, particularly for inquiries about radiation exposure and the use of lasers in MISS. Conclusions While ChatGPT is proficient in providing clinically appropriate responses to certain FAQs about MISS, it frequently produces responses that exceed the recommended readability level for patient education. This limitation suggests that its utility may be confined to highly educated patients, potentially exacerbating existing disparities in patient comprehension. Future AI-based patient education tools must prioritize clear and accessible communication, with oversight from medical professionals to ensure accuracy and appropriateness. Further research comparing ChatGPT's performance with other AI models could enhance its application in patient education across medical specialties.

Indexed as

aichatgptminimally invasive spine surgerypatient educationreadability

Identifiers

PMID39525124
PMCPMC11548952

What OpenQuestion holds

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LicenceCC BY
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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.