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TRUVACE RECORD VERSION
record: TRV-2026-1604
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-16T18:14:19.453749Z
status: published
lens: g_space
sector: health
headline: Evaluation of an Artificial Intelligence-Driven Language Model for Age-Specific Patient Education in Pediatric Orthopaedics
dek: Background: Describing complex orthopaedic surgical procedures and diagnoses to pediatric patients across a large age range and at varying levels of health literacy can be quite challenging. Generative artificial intelligence (GenAI) offers a potential solution by providing real-time age specific explanations for pediatric patients. The use of GenAI large language models (LLMs) could potentially allow providers to more effectively communicate complex medical problems in an age-appropriate and comprehensible mann…
gain_title: ChatGPT-4 produced explanations that became progressively less readable for older age prompts, and seven pediatric orthopaedic surgeons rated their accuracy highly and felt comfortable using them in clinical settings.
problem_title: (none)
trace_subject: (none)
gain_reading: ChatGPT-4 produced explanations that became progressively less readable for older age prompts, and seven pediatric orthopaedic surgeons rated their accuracy highly and felt comfortable using them in clinical settings.
gain_evidence: The 6-year-old prompts were more readable than the 10- and 14-year-old prompts | the mean (standard deviation) attending-rated accuracy for the artificial intelligence responses was 4.15 (0.81) | demonstrated a high level of comfort using the GenAI explanations in clinical settings
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers prompted ChatGPT-4 to explain 43 pediatric orthopaedic procedures and tests for imagined patients aged 6, 10 and 14. Six quantitative readability indices scored the outputs, and seven board-certified pediatric orthopaedic surgeons rated accuracy and comfort, comparing the text with their own clinical explanations.

The findings suggest a language model could help clinicians explain complex care to children with differing health literacy, supporting shared decision-making. The evidence is a physician review of generated text rather than a trial with real patients, so effects on children's understanding or outcomes remain untested.
limitation: 
tag: Evidence-backed gain
key_points: All six readability indices differed significantly across the 6-, 10- and 14-year-old prompts (P < .001). | Younger-age prompts yielded the most readable text, and 14-year-old prompts the least readable. | Mean attending-rated accuracy was 4.15 out of 5, with no significant differences by procedure (P = .051) or age group (P = .27). | Mean attending-rated comfort was 3.95, with inter-rater reliability of 0.92 for accuracy and 0.94 for comfort.
rundown: The study tested ChatGPT-4 on 43 pediatric orthopaedic procedures and tests, with each one prompted for three ages: 6, 10 and 14 years. This design allowed comparison of how the model adjusted its language across a wide developmental range.

Readability was measured with six quantitative indices, and every index showed significant separation between the age groups. Seven board-certified pediatric orthopaedic surgeons separately scored accuracy and comprehensiveness and compared the output with the explanations they use in practice.

The authors conclude that, when reviewed by a physician, generative AI could be a helpful tool for patient education, health literacy and shared decision-making. The study is labeled level of evidence III/IV.
sources:
- peer_reviewed | Journal of the Pediatric Orthopaedic Society of North America | https://doi.org/10.1016/j.jposna.2026.100447 | 2026-08-21
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