: Upper blepharoplasty addresses aging-related changes by removing excess skin, muscle, and fat from the upper eyelids. This systematic review, which was registered a priori, improves upon previously published reviews regarding functional and aesthetic outcomes following upper blepharoplasty. Custom search strategies were applied across multiple databases, namely MEDLINE (United States National Library of Medicine, Bethesda, MD), Embase (Elsevier, Amsterdam, the Netherlands), Google Scholar (Alphabet, Inc., Mountain View, CA), Cochrane Central Register of Controlled Trials (CENTRAL; Cochrane Library, London, UK), Science Citation Index (Clarivate Analytics, Philadelphia, PA), PubMed (United States National Library of Medicine), and PsychInfo (American Psychological Association, Washington, DC). Bias was assessed with the Cochrane RoB 2 tool, and methodological quality was evaluated with GRADE. Out of 6799 articles, 12 randomized controlled trials with 450 patients (39 men, 411 women) were included. The meta-analysis showed significant reduction in dry eye symptoms post–upper blepharoplasty compared with preoperative status (odds ratio [OR], 0.22; 95% CI, 0.13-0.36; P < .00001). No significant difference in postoperative dry eye incidence was found between orbicularis oculi muscle plus skin excision and skin-only excision (OR, 1.55; 95% CI, 0.86-2.80; P = .25). However, muscle plus skin excision had a higher incidence of lagophthalmos (OR, 7.98; 95% CI, 1.41-45.21; P = .02). No significant differences were observed in eye irritation (OR, 1.21; 95% CI, 0.51-2.84; P = .66) or patient satisfaction (OR, 1.14; 95% CI, 0.42-3.14; P = .80). Continuous suturing techniques increased eye irritation compared with subcuticular suturing (OR, 1.77; 95% CI, 1.07-2.91; P = .03). All studies were high quality with a low risk of bias. The findings support upper blepharoplasty's efficacy in reducing dry eye symptoms, with muscle-sparing techniques minimizing lagophthalmos and maintaining high patient satisfaction. Level of Evidence: 3:

Purpose: Biological augmentation of anterior cruciate ligament (ACL) reconstruction with bone marrow aspirate concentrate (BMAC) is gaining attention for its theoretical potential to enhance postoperative healing and recovery. However, its clinical benefits remain uncertain, and its high cost raises questions about efficacy. Hence, we systematically reviewed randomised controlled trials (RCTs) to evaluate the effectiveness of BMAC in ACL reconstruction. Methods: Our search included Cochrane, EMBASE, OVID, PubMed, and Scopus databases for RCTs evaluating the use of BMAC in ACL reconstruction. Primary outcomes focused on International Knee Documentation Committee (IKDC) scores and Lysholm scores. Secondary outcomes included MRI-related outcomes and postoperative complications. Statistical analysis was conducted using Review Manager 5.4 (Cochrane Collaboration), with heterogeneity assessed using Cochrane’s Q test and I² statistics. Results: 221 patients from five RCTs were included, with 109 (49.3%) receiving BMAC augmentation. Follow-up ranged from 11.05 to 24 months. No significant differences were found in postoperative IKDC scores between the BMAC and control groups at, three, six and 12 months. The BMAC group had significantly higher IKDC scores at 24 months; however, this difference was unlikely to be clinically significant. No significant differences were observed in postoperative Lysholm scores at 12 or 24 months. MRI-related outcomes suggested potential graft recovery improvement with BMAC, and complication rates were comparable between groups. Conclusion: In summary, biological augmentation with BMAC in ACL reconstruction does not significantly improve early patient-reported outcomes but offers potential benefits in graft recovery without increasing complication rates.

Artificial intelligence (AI) has emerged as a transformative field in computational research with diverse applications in medicine, particularly in the field of liver transplantation (LT) given its ability to analyze and build upon complex and multidimensional data. This literature review investigates the application of AI in LT, focusing on its role in pre-implantation biopsy evaluation, development of recipient prognosis algorithms, imaging analysis, and decision-making support systems, with the findings revealing that AI can be applied across a variety of fields within LT, including diagnosis, organ allocation, and surgery planning. As a result, algorithms are being developed to assess steatosis in pre-implantation biopsies and predict liver graft function, with AI applications displaying great accuracy across various studies included in this review. Despite its relatively recent introduction to transplantation, AI demonstrates potential in delivering cost and time-efficient outcomes. However, these tools cannot replace the role of healthcare professionals, with their widespread adoption demanding thorough clinical testing and oversight.

Background: In the UK, 43% of adults struggle to understand health information presented in standard formats. As a result, Health Education England recommends that patient information leaflets (PILs) be written at a readability level appropriate for an 11-year-old. Objectives: To evaluate the ability of ChatGPT-4 and its three dermatology-specific plugins to generate PILs that meet readability recommendations and compare their readability with existing British Association of Dermatologists (BAD) PILs. Methods: ChatGPT-4 and its three plugins were used to generate PILs for 10 preselected dermatological conditions. The readability of these PILs was assessed using three readability formulas Simple Measure of Gobbledygook (SMOG), Flesch Reading Ease Test (FRET) and Flesch–Kincaid Grade Level Test (FKGLT) and compared against the readability of BAD PILs. A one-way ANOVA was conducted to identify any significant differences. Results: The readability scores of PILs generated by ChatGPT-4 and its plugins did not meet the recommended target range. However, some of these PILs demonstrated more favourable mean readability scores compared with those from the BAD, with certain plugins, such as Chat with a Dermatologist, showing significant differences in mean SMOG (P = 0.0005) and mean FKGLT (P = 0.002) scores. Nevertheless, the PILs generated by ChatGPT-4 were found to lack some of the content typically included in BAD PILs. Conclusions: ChatGPT-4 can produce dermatological PILs free from misleading information, occasionally surpassing BAD PILs in terms of readability. However, these PILs still fall short of being easily understood by the general public, and the content requires rigorous verification by healthcare professionals to ensure reliability and quality.