Efficacy of emergency extracorporeal shock wave lithotripsy in the treatment of ureteral stones: a meta-analysis.

OBJECTIVE: To compare the clinical efficiency and safety of emergency extracorporeal shock wave lithotripsy (eESWL) and delayed extracorporeal shock wave lithotripsy (dESWL) in the treatment of ureteral stones. METHODS: Cochrane Library, PubMed, Google Scholar, and Web of Science were searched from January 1, 1992 to September 30, 2022, and all comparative studies involving eESWL and dESWL for ureteral calculi were included. Statistical analysis was performed using Review Manager 5.3 software. Funnel plot was used to evaluated publication bias. RESULTS: A total of 9 articles involving 976 patients diagnosed with ureteral stones were included. The results showed that the stone-free rate (SFR) after four weeks was significantly higher in the eESWL group than in the dESWL group [relative risk (RR) = 1.22, 95% confidence interval (CI): 1.13-1.32, P < 0.01]. In subgroup analysis of different stone locations, proximal ureteral calculi [RR = 1.25, 95% CI: 1.14-1.38, P < 0.01] and mid-to-distal ureteral calculi [RR = 1.18, 95% CI: 1.03-1.34, P < 0.05] all showed a higher SFR in the eESWL group. eESWL significantly shortened the stone-free time(SFT) [mean difference (MD) = -5.75, 95% CI: -9.33 to -2.17, P < 0.01]. In addition, eESWL significantly reduced auxiliary procedures [RR = 0.53, 95% CI: 0.40-0.70, P < 0.01]. No significant difference in complications was found between the two groups [RR = 0.90, 95% CI: 0.69-1.16, P > 0.05]. CONCLUSION: eESWL can significantly improve SFR, shorten SFT, and reduce auxiliary procedures.

Comparison of the long-term follow-up and perioperative outcomes of partial nephrectomy and radical nephrectomy for 4 cm to 7 cm renal cell carcinoma: a systematic review and meta-analysis.

BACKGROUND: The efficacy of partial nephrectomy (PN) for T1b renal cell carcinoma (RCC) is controversial. The oncological outcomes, the change in postoperative renal function and the perioperative complications are unclear. METHODS: We searched PUBMED, EMBASE and the Cochrane Central Register for studies from March 1998 to March 2018 for studies comparing PN to radical nephrectomy (RN) for the treatment of T1b RCC. After data extraction and quality assessment, we used RevMan 5.2 to pool the data. Then, we used Stata 12.0 to perform sensitivity analyses and meta-regression. We used the GRADE profiler to evaluate the evidence according to the GRADE approach. RESULTS: A total of 16 studies involving 33,117 patients were included in our meta-analysis. No significant difference was found in the 5-year overall survival (OS), 10-year OS, 5-year recurrence-free survival (RFS) and 10-year RFS. The 5-year cancer-special survival (CSS) and 10-year CSS were better in RN compared to PN, respectively, at RR = 1.02, P < 0.05 and RR = 1.04, P < 0.05. PN was better than RN in the preservation of renal function (WMD = -9.15, 95% CI: - 10.30 to - 7.99, P < 0.05). The confidence level grading of the evidence was moderate for 5-year OS, 10-year OS, 5-year CSS, 10-year CSS, 5-year RFS, 10-year RFS, tumor recurrence, decline in eGFR, and postoperative complications. CONCLUSIONS: PN may provide comparable outcomes in terms of RFS & OS, and better renal function preservation although CSS was worse.

Sentiment Analysis Methods for HPV VaccinesRelated Tweets Based on Transfer Learning.

The widespread use of social media provides a large amount of data for public sentimentanalysis. Based on social media data, researchers can study public opinions on humanpapillomavirus (HPV) vaccines on social media using machine learning-based approaches that willhelp us understand the reasons behind the low vaccine coverage. However, social media data isusually unannotated, and data annotation is costly. The lack of an abundant annotated dataset limitsthe application of deep learning methods in effectively training models. To tackle this problem, wepropose three transfer learning approaches to analyze the public sentiment on HPV vaccines onTwitter. One was transferring static embeddings and embeddings from language models (ELMo)and then processing by bidirectional gated recurrent unit with attention (BiGRU-Att), called DWEBiGRU-Att. The others were fine-tuning pre-trained models with limited annotated data, called finetuninggenerative pre-training (GPT) and fine-tuning bidirectional encoder representations fromtransformers (BERT). The fine-tuned GPT model was built on the pre-trained generative pretraining(GPT) model. The fine-tuned BERT model was constructed with BERT model. Theexperimental results on the HPV dataset demonstrated the efficacy of the three methods in thesentiment analysis of the HPV vaccination task. The experimental results on the HPV datasetdemonstrated the efficacy of the methods in the sentiment analysis of the HPV vaccination task. Thefine-tuned BERT model outperforms all other methods. It can help to find strategies to improvevaccine uptake.