ObjectivesThe present network meta-analysis was conducted to evaluate the potential efficacy and safety of various surgical approaches in the treatment of benign prostatic hyperplasia with enlarged prostate.MethodsPubMed, EMbase, The Cochrane Library, Clinicaltrials.gov and CNKI databases were electronically searched to identify eligible studies. Two reviewers independently screened literature, extracted data and evaluated risk of bias and the ADDIS 1.16.8 software was used to conduct meta-analysis.ResultsA total of 23 studies involving 2 849 patients with 5 approaches including open prostatectomy (OP), holmium laser enucleation of the prostate (HoLEP), plasmakinetic/bipolar plasmakinetic enucleation of the prostate (PK/BPEP), transurethral vaporization of the prostate (TUVP), and laparoscopic prostatectomy (LSP) were included. HoLEP, PK/BPEP and OP were superior to the other methods in improving the objective indicators and subjective feelings of patients during both short and medium-term follow-up. However, compared with OP, HoLEP and PK/BPEP were observed to result in a significantly lower hemoglobin level (MD=1.65, 95%CI 0.35 to 4.41; MD=2.62, 95%CI 0.64 to 2.90), longer postoperative irrigation time (MD=4.67, 95%CI 1.29 to 10.66; MD=2.67, 95%CI 1.32 to 6.63), as well as indwelling catheter after operation (MD=1.64, 95%CI 0.48 to 4.15; MD=2.52, 95%CI 0.60 to 3.78). In terms of short-term complications, PK/BPEP (RR=0.45, 95%CI 0.13 to 1.29) was found to be significantly lower than that of OP.ConclusionsHoLEP and PK/BPEP can be probably used as a superior treatment option for large volume benign prostatic hyperplasia because of its better curative effect, higher safety and quick postoperative recovery.
Objective To evaluate medical students’ perceptions and attitudes toward artificial intelligence (AI)-assisted diagnosis of renal cell carcinoma (RCC), and to analyze their educational needs regarding AI in pathological diagnosis. Methods A questionnaire survey (including closed and open-ended questions) was conducted to assess medical students’ perceptions, attitudes, and educational needs concerning AI-assisted RCC diagnosis. Participants included medical students from different specialties and standardized training residents. The questionnaire covered demographic information, perceptions and attitudes toward AI, and AI-related educational needs. Results A total of 249 respondents completed the survey. The majority were standardized training residents, mostly aged 23-26 years, and 40.96% had practical experience in pathological diagnosis of RCC. The median scores for most closed-ended questions were 4. Respondents generally considered “efficiency” and “improved accuracy” as the most prominent advantages of AI, with timeliness, automated diagnosis, reduction of human error, and precise diagnosis being the most emphasized aspects. Analysis of AI-related educational needs revealed high-frequency keywords such as “expanding sample size” “balanced responsibility allocation” and “enhancing collaboration skills.” Conclusion Medical students hold a positive attitude toward AI and its application in RCC diagnosis, but there remains a lack of formal AI-related education.