Are Generative Pretrained Transformer 4 Responses to Developmental Dysplasia of the Hip Clinical Scenarios Universal? An International Review
Bibliographic record
Abstract
OBJECTIVE: There is increasing interest in applying artificial intelligence chatbots like generative pretrained transformer 4 (GPT-4) in the medical field. This study aimed to explore the universality of GPT-4 responses to simulated clinical scenarios of developmental dysplasia of the hip (DDH) across diverse global settings. METHODS: Seventeen international experts with more than 15 years of experience in pediatric orthopaedics were selected for the evaluation panel. Eight simulated DDH clinical scenarios were created, covering 4 key areas: (1) initial evaluation and diagnosis, (2) initial examination and treatment, (3) nursing care and follow-up, and (4) prognosis and rehabilitation planning. Each scenario was completed independently in a new GPT-4 session. Interrater reliability was assessed using Fleiss kappa, and the quality, relevance, and applicability of GPT-4 responses were analyzed using median scores and interquartile ranges. Following scoring, experts met in ZOOM sessions to generate Regional Consensus Assessment Scores, which were intended to represent a consistent regional assessment of the use of the GPT-4 in pediatric orthopaedic care. RESULTS: GPT-4's responses to the 8 clinical DDH scenarios received performance scores ranging from 44.3% to 98.9% of the 88-point maximum. The Fleiss kappa statistic of 0.113 ( P = 0.001) indicated low agreement among experts in their ratings. When assessing the responses' quality, relevance, and applicability, the median scores were 3, with interquartile ranges of 3 to 4, 3 to 4, and 2 to 3, respectively. Significant differences were noted in the prognosis and rehabilitation domain scores ( P < 0.05 for all). Regional consensus scores were 75 for Africa, 74 for Asia, 73 for India, 80 for Europe, and 65 for North America, with the Kruskal-Wallis test highlighting significant disparities between these regions ( P = 0.034). CONCLUSIONS: This study demonstrates the promise of GPT-4 in pediatric orthopaedic care, particularly in supporting preliminary DDH assessments and guiding treatment strategies for specialist care. However, effective integration of GPT-4 into clinical practice will require adaptation to specific regional health care contexts, highlighting the importance of a nuanced approach to health technology adaptation. LEVEL OF EVIDENCE: Level IV.
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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 | 0.022 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".