Evidence map›Paper›PMID 38112605›Full record

ArticleEuropean journal of translational myology2023

ChatGPT in the development of medical questionnaires. The example of the low back pain.

Daniele Coraci, Maria Chiara Maccarone, Gianluca Regazzo, Giorgia Accordi, Jannis V Papathanasiou, Stefano Masiero

Abstract read
In one paragraph

Article in European journal of translational myology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 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.

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

16 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025
    Pooled it
  2. Pooled it
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Adoption of blockchain as a step forward in orthopedic practice.European journal of translational myology · 2024
    Article
  15. Article
  16. 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.

Daniele CoraciDepartment of Neuroscience, Section of Rehabilitation, University of Padova, Padua. danielecoraci@aol.com.
Maria Chiara MaccaroneDepartment of Neuroscience, Section of Rehabilitation, University of Padova, Padua. mariachiara.maccarone93@gmail.com.
Gianluca RegazzoDepartment of Neuroscience, Section of Rehabilitation, University of Padova, Padua. gianlucareg@gmail.com.
Giorgia AccordiDepartment of Neuroscience, Section of Rehabilitation, University of Padova, Padua. giorgia.accordi@studenti.unipd.it.
Jannis V PapathanasiouDepartment of Kinesiotherapy, Faculty of Public Health, Medical University of Sofia, Sofia, Bulgaria; Department of Medical Imaging, Allergology and Physiotherapy, Faculty of Dental Medicine, Medical University of Plovdiv, Plovdiv. giannipap@yahoo.co.uk.
Stefano MasieroDepartment of Neuroscience, Section of Rehabilitation, University of Padova, Padua. stef.masiero@unipd.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the last year, Chat Generative Pre-Trained Transformer (ChatGPT), a web software based on artificial intelligence has been showing high potential in every field of knowledge. In the medical area, its possible application is an object of many studies with promising results. We performed the current study to investigate the possible usefulness of ChatGPT in assessing low back pain. We asked ChatGPT to generate a questionnaire about this clinical condition and we compared the obtained questions and results with the ones obtained by other validated questionnaires: Oswestry Disability Index, Quebec Back Pain Disability Scale, Roland-Morris Disability Questionnaire, and Numeric Rating Scale for pain. We enrolled 20 subjects with low back pain and we found important consistencies among the validated questionnaires. The ChatGPT questionnaire showed an acceptable significant correlation only with Oswestry Disability Index and Quebec Back Pain Disability Scale. ChatGPT showed some peculiarities, especially in the assessment of quality of life and medical consultation and treatments. Our study shows that ChatGPT can help evaluate patients, including multilevel perspectives. However, its power is limited, and further research and validation are required.

Identifiers

PMID38112605
PMCPMC10811646

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.