Meta-analysis of the complications of laparoscopic renal surgery: comparison of procedures and techniques

PURPOSE: We performed a meta-analysis of the literature to define the current expectations of complications during laparoscopic renal surgery. MATERIALS AND METHODS: References were searched in the MEDLINE database from 1995 to 2004 using the terms complications and laparoscopic nephrectomy. Inclusion criteria were any series with greater than 20 cases, patient age older than 16 years and any complications listed for certain procedures, including laparoscopic radical nephrectomy, HA laparoscopic radical nephrectomy, LPN, HALPN, laparoscopic donor nephrectomy, HA laparoscopic donor nephrectomy, laparoscopic simple nephrectomy, laparoscopic nephroureterectomy and retroperitoneal laparoscopic nephrectomy. A data extraction form was created to categorize major or minor complications. A 5 member panel adhered to the strict criteria and extracted data from articles that met inclusion criteria. Data were entered into a spreadsheet and a meta-analysis was performed. RESULTS: Initial review identified 73 of 405 references that were acceptable for retrieval and data extraction, of which 56 met inclusion criteria. The overall major and minor complication rates of laparoscopic renal surgery were 9.5% and 1.9%, respectively. There was a significant difference between the major complication rates of LPN and HALPN (21.0% vs 3.3%, p <0.05). CONCLUSIONS: Our results show that patients who undergo laparoscopic renal surgery may have an overall major complication rate of 9.5%. The highest major complication rate is associated with technically challenging LPN (21%). There appears to be a significantly higher wound complication rate associated with HA surgery in comparison to that of standard laparoscopy (1.9% vs 0.2%, p <0.05).     

Dental caries experience among kindergarten school going children of,India

ObjectiveThe aim of the present study was to estimate the prevalence of dental caries in the primary dentition of kindergarten school children of Udaipur city Rajasthan in a cross sectional study design.Materials and methodsStudy population comprised of around 355 children aged 3–6 years old randomly selected from 10 kindergarten schools in Udaipur city from all preselected five zones. Dental caries was recorded using the decayed, missing, filled teeth [dmf-t] index and the decayed, missing, filled surfaces [dmf-s] index according to WHO 1997 standards.ResultsSeventy-nine point one five percent are caries free individuals while 20.85% are individuals having caries. The mean dt scores for the age group six is found to be highest with 0.45 (0.86) and least for the age group of four with 0.28 (0.74). Sixty-five point six three percent of the subjects brushed their teeth by themselves while 34.37% do it with the help of their mothers.ConclusionDental caries prevalence and severity is low among the students.     

Machine Learning for Detection of Correct Peripherally Inserted Central Catheter Tip Position from Radiology Reports in Infants

Background  In critically ill infants, the position of a peripherally inserted central catheter (PICC) must be confirmed frequently, as the tip may move from its original position and run the risk of hyperosmolar vascular damage or extravasation into surrounding spaces. Automated detection of PICC tip position holds great promise for alerting bedside clinicians to noncentral PICCs. Objectives  This research seeks to use natural language processing (NLP) and supervised machine learning (ML) techniques to predict PICC tip position based primarily on text analysis of radiograph reports from infants with an upper extremity PICC. Methods  Radiographs, containing a PICC line in infants under 6 months of age, were manually classified into 12 anatomical locations based on the radiologist''s textual report of the PICC line''s tip. After categorization, we performed a 70/30 train/test split and benchmarked the performance of seven different (neural network, support vector machine, the naïve Bayes, decision tree, random forest, AdaBoost, and K-nearest neighbors) supervised ML algorithms. After optimization, we calculated accuracy, precision, and recall of each algorithm''s ability to correctly categorize the stated location of the PICC tip. Results  A total of 17,337 radiographs met criteria for inclusion and were labeled manually. Interrater agreement was 99.1%. Support vector machines and neural networks yielded accuracies as high as 98% in identifying PICC tips in central versus noncentral position (binary outcome) and accuracies as high as 95% when attempting to categorize the individual anatomical location (12-category outcome). Conclusion  Our study shows that ML classifiers can automatically extract the anatomical location of PICC tips from radiology reports. Two ML classifiers, support vector machine (SVM) and a neural network, obtained top accuracies in both binary and multiple category predictions. Implementing these algorithms in a neonatal intensive care unit as a clinical decision support system may help clinicians address PICC line position.