EP64 Assessment of Quality and Readability of Information Provided by ChatGPT in Relation to Developmental Dysplasia of the Hip and Periacetabular Osteotomy
Bibliographic record
Abstract
Abstract Background This study evaluates the quality and readability of responses given by ChatGPT 4 relating to common patient queries on Developmental Dysplasia of the Hip (DDH) and Periacetabular Osteotomy (PAO). Methods Frequently asked questions on DDH and PAO were selected from online Patient Education Materials and posed to ChatGPT 4. The responses were evaluated by four high-volume PAO surgeons using a well-established evidence-based rating system, categorizing responses from 'excellent response not requiring clarification' to 'unsatisfactory requiring substantial clarification.' Readability assessments were subsequently conducted to determine the required literacy level to understand the content provided. Results Responses from ChatGPT 4 varied significantly between preoperative and postoperative queries. In the postoperative category, 50% of responses were rated as 'excellent,' showing no need for further clarification, while the preoperative responses frequently required minimal to moderate clarification. The overall median response rating was 'satisfactory requiring minimal clarification.' Readability tests showed that the average Reading Grade Level was 13.44, considerably higher than the recommended 6th grade level for patient education materials, indicating a substantial barrier to comprehension for the general public. Conclusions While ChatGPT delivers generally reliable information, the complexity of its language is a major barrier to widespread utilization as a tool for patient education. Future iterations of ChatGPT should aim to utilize more simplistic language, as such enhancing accessibility without compromising content quality.
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 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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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".