Global insights into keloid formation: An international systematic review of regional genetic risk factors and commonalities.

Keloid disorder is a morbid and disfiguring benign fibroproliferative disease with a higher incidence in groups with darker skin pigmentation. Predicting keloidogenesis in patients is difficult with treatment primarily aimed at preventing further scar expansion and improving aesthetics without addressing their unknown underlying pathophysiology. We aimed to identify potential genetic predispositions to keloid scarring in the literature. A search was conducted on 21 August 2023, by the first and second authors independently from 1985 to August 2023 using PubMed, MEDLINE, Embase, Web of Science, Scopus and CINAHL. The following MeSH terms were used: 'Keloid', 'Risk' and 'Genetic'. Two researchers independently searched for studies based on titles and abstracts and screened filtered articles by reviewing full text. If no agreement could be reached, a third senior author designated whether the article should be included. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement as the basis of our organisation. Human studies with genetic analysis to determine an association of a protein or gene to keloidogenesis were selected for inclusion. Studies in languages other than English, reviews, conference articles, and book chapters were excluded. Fifty studies met inclusion criteria. The human leukocyte antigen (HLA) system was broadly implicated, and the DRB1*15 allele was associated with an increased risk of keloid in three separate ethnic groups. Some HLA Class I alleles were associated with keloid in one population but not in others. Additionally, polymorphisms in the E3 ubiquitin-protein ligase (NEDD4) signal cascade and vitamin D receptor (VDR) have been implicated in diverse groups. No current genetic test can predict keloid risk. Our review identified candidate predisposing genes, including NEDD4, VDR and components of the HLA system. Further studies in heterogeneous populations are needed to identify reliable screening targets.

Addressing Current Deficits in Patient Education Materials Through Crowdsourcing.

BACKGROUND: Patient education materials are commonly reported to be difficult to understand. OBJECTIVES: We aimed to use crowdsourcing to improve patient education materials at our institution. METHODS: This was a department-wide quality improvement project to increase organizational health literacy. There are 6 phases of this pilot study: (1) evaluating preexisting patient education materials, (2) evaluating online patient education materials at the society (the American Society of Plastic Surgeon) and government level (Medline Plus), (3) redesigning our patient education material and reevaluating the education material, (4) crowdsourcing to evaluate understandability of the new patient education material, (5) data analysis, and (6) incorporating crowdsourcing suggestions to the patient education material. RESULTS: Breast-related patient education materials are not easy to read at the institution level, the society level, and the government level. Our new implant-based breast reconstruction patient education material is easy to read as demonstrated by the crowdsourcing evaluation. More than 90% of the participants reported our material is "very easy to understand" or "easy to understand." The crowdsourcing process took 1.5 days, with 700 workers responding to the survey. The total cost was $9. After incorporating participants' feedback into the finalized material, the readability of the material is at the recommended reading level. The material also had the recommended length (between 400 and 800 words). DISCUSSION: Our study demonstrated a pathway for clinicians to efficiently obtain a large amount of feedback to improve patient education materials. Crowdsourcing is an effective tool to improve organizational health literacy.

Comparison of Patient Education Materials Generated by Chat Generative Pre-Trained Transformer Versus Experts: An Innovative Way to Increase Readability of Patient Education Materials.

INTRODUCTION: Improving patient education materials may improve patient outcomes. This study aims to explore the possibility of generating patient education materials with the assistance of a large language model, Chat Generative Pre-Trained Transformer (ChatGPT). In addition, we compare the accuracy and readability of ChatGPT-generated materials versus expert-generated materials. METHODS: Patient education materials in implant-based breast reconstruction were generated by experts and ChatGPT independently. Readability and accuracy of the materials are the main outcomes. Readability of the materials was compared using Flesch-Kincaid score. Accuracy of the materials generated by ChatGPT was evaluated by 2 independent reviewers. Content errors are categorized into information errors, statistical errors, and multiple errors (errors more than 2 types). RESULTS: The content generated by experts had higher readability. The Flesch-Kincaid score is at the 7.5 grade for expert-generated materials, whereas the content generated by ChatGPT is at the 10.5 grade (despite ChatGPT being asked to generate content at the seventh grade level). The accuracy of ChatGPT-generated content is 50%, with most errors being information errors. ChatGPT often provides information about breast reduction or breast augmentation, despite being asked specifically about breast reconstruction. Despite its limitation, ChatGPT significantly reduced the time required to generate patient education materials. Although it takes experts 1 month to generate patient education materials, ChatGPT generates materials within 30 minutes. CONCLUSIONS: ChatGPT can be a powerful starting tool to generate patient education materials. However, its readability and accuracy still require improvements.