Exploring the ability of ChatGPT to create quality patient education resources about kidney transplant
Bibliographic record
Abstract
BACKGROUND: Chat Generative Pre-trained Transformer (ChatGPT) is a language model that may have the potential to revolutionize health care. The study purpose was to test whether ChatGPT could be used to create educational brochures about kidney transplant tailored for three target audiences: caregivers, teens and children. METHODS: Using a list of 25 educational topics, standardized prompts were employed to ensure content consistency in ChatGPT generation. An expert panel assessed the accuracy of the content by rating agreement on a Likert scale (1 = <25 % agreement; and 5 = 100 % agreement). The understandability, actionability and readability of the brochures were assessed using the Patient Education Materials Assessment Tool for printable materials (PEMAT-P) and standard readability scales. A caregiver and patient reviewed and provided written feedback. RESULTS: We found mean understandability scores of 69 %, 66 %, and 73 % for caregiver, teen, and child brochures respectively, with 90.7 % of the ChatGPT generated brochures scoring 40 % on the actionability scale. Generated caregiver and teen materials achieved readability levels of grades 9-14, while child-specific brochures achieved readability levels of grades 6-11. Brochures were formatted appropriately but lacked depth. CONCLUSION: ChatGPT demonstrates potential for rapidly generating patient education materials; however, challenges remain in ensuring content specificity. We share the lessons learned to assist other healthcare providers with using this technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".