ArticleJB & JS open access
Source Characteristics Influence AI-Enabled Orthopaedic Text Simplification: Recommendations for the Future.
Article in JB & JS open access. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review.Journal of medical Internet research · 2026Article
- Evaluation of large language model performance in translating dairy-related content.Translational animal science · 2026Article
- An Organized Approach to Using Large Language Models for Medical Information.The western journal of emergency medicine · 2025Article
- PatientEase-Domain-Aware RAG for Rehabilitation Instruction Simplification.Bioengineering (Basel, Switzerland) · 2025Article
- Refining patient education on autologous chondrocyte implantation for chondral lesions of the knee: A fine-tuned ChatGPT-4o model improves readability and quality.Journal of experimental orthopaedics · 2025Article
- The role of large language models in improving the readability of orthopaedic spine patient educational material.Journal of orthopaedic surgery and research · 2025Article
- Using AI to Translate and Simplify Spanish Orthopedic Medical Text: Instrument Validation Study.JMIR AI · 2025Article
- Language Complexity Shapes Engagement with Orthopaedic Information on Social Media: A Prospective Analysis.JB & JS open accessArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study assesses the effectiveness of large language models (LLMs) in simplifying complex language within orthopaedic patient education materials (PEMs) and identifies predictive factors for successful text transformation. Methods: We transformed 48 orthopaedic PEMs using GPT-4, GPT-3.5, Claude 2, and Llama 2. The readability, quantified by the Flesch-Kincaid Reading Ease (FKRE) and Flesch-Kincaid Grade Level (FKGL) scores, was measured before and after transformation. Analysis included text characteristics such as syllable count, word length, and sentence length. Statistical and machine learning methods evaluated the correlations and predictive capacity of these features for transformation success. Results: All LLMs improved FKRE and FKGL scores (p < 0.01). GPT-4 showed superior performance, transforming PEMs to a seventh-grade reading level (mean FKGL, 6.72 ± 0.99), with higher FKRE and lower FKGL than other models. GPT-3.5, Claude 2, and Llama 2 significantly shortened sentences and overall text length (p < 0.01). Importantly, correlation analysis revealed that transformation success varied substantially with the model used, depending on original text factors such as word length and sentence complexity. Conclusions: LLMs successfully simplify orthopaedic PEMs, with GPT-4 leading in readability improvement. This study highlights the importance of initial text characteristics in determining the effectiveness of LLM transformations, offering insights for optimizing orthopaedic health literacy initiatives using artificial intelligence (AI). Clinical Relevance: This study provides critical insights into the ability of LLMs to simplify complex orthopaedic PEMs, enhancing their readability without compromising informational integrity. By identifying predictive factors for successful text transformation, this research supports the application of AI in improving health literacy, potentially leading to better patient comprehension and outcomes in orthopaedic care.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.