ChatGPT in the development of medical questionnaires. The example of the low back pain
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".