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Purpose: Mutations in Topoisomerase I-binding RS protein (TOPORS) have been previously documented and have been described to result in pathological autosomal dominant retinitis pigmentosa (adRP). In our study, we describe the various genotypes and clinical/phenotypic manifestations of TOPORS-related mutations of our unique patient population in Rural Appalachia. Methods: The medical records of 416 patients with inherited retinal disease at the West Virginia University Eye Institute who had undergone genetic testing between the years of 2015-2022 were reviewed. Patients found to have pathologic RP and mutations related to TOPORS were then analyzed. Results: In total, 7 patients (ages 12-70) were identified amongst three unique families. All patients were female in our study. The average follow-up period was 7.7 years. A mother (70 yr) and daughter (51 yr) had a novel heterozygous nonsense point mutation in TOPORS c.2431C > T, p.Gln811X (Exon 3) that led to premature termination of the desired protein resulting in early onset vision loss, cataract formation, and visual field restriction. The mother developed a full-thickness macular hole which was successfully repaired. Five other patients were found to have previously described TOPORS mutations. Visual field loss was progressive with age in both cohorts. Conclusions: Seven patients at our institution were identified to have mutations in TOPORS resulting in autosomal dominant retinitis pigmentosa. Two patients were found to have novel truncating mutations in the TOPORS gene resulting in profound night blindness and visual field loss, recurrent macular edema, and in one individual, epiretinal membrane formation leading to a macular hole which was able to be successfully repaired.
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PURPOSE: This study aims to compare the readability of patient education materials (PEM) of the American Society of Ophthalmic Plastic and Reconstructive Surgery to that of PEMs generated by the AI-chat bots ChatGPT and Google Bard. METHODS: PEMs on 16 common American Society of Ophthalmic Plastic and Reconstructive Surgery topics were generated by 2 AI models, ChatGPT 4.0 and Google Bard, with and without a 6th-grade reading level prompt modifier. The PEMs were analyzed using 7 readability metrics: Flesch Reading Ease Score, Gunning Fog Index, Flesch-Kincaid Grade Level, Coleman-Liau Index, Simple Measure of Gobbledygook Index Score, Automated Readability Index, and Linsear Write Readability Score. Each AI-generated PEM was compared with the equivalent American Society of Ophthalmic Plastic and Reconstructive Surgery PEM. RESULTS: Across all readability indices, PEM generated by ChatGPT 4.0 consistently had the highest readability scores, indicating that the material generated by this AI chatbot may be most difficult to read in its unprompted form (Flesch Reading Ease Score: 36.5; Simple Measure of Gobbledygook: 14.7). Google's Bard was able to generate content that was easier to read than both the American Society of Ophthalmic Plastic and Reconstructive Surgery and ChatGPT 4.0 (Flesch Reading Ease Score: 52.3; Simple Measure of Gobbledygook: 12.7). When prompted to produce PEM at a 6th-grade reading level, both ChatGPT 4.0 and Bard were able to significantly improve in their readability scores, with prompted ChatGPT 4.0 being able to consistently generate content that was easier to read (Flesch Reading Ease Score: 67.9, Simple Measure of Gobbledygook: 10.2). CONCLUSION: This study suggests that AI tools, when guided by appropriate prompts, can generate accessible and comprehensible PEMs in the field of ophthalmic plastic and reconstructive surgeries, balancing readability with the complexity of the necessary information.
