DRAIN VERSUS NO DRAIN AFTER LAPAROSCOPIC CHOLECYSTECTOMY-A COMPARATIVE STUDY.

INTRODUCTION Gallstones are the most common conditions encountered in surgical OPD. The prevalence rose with age, except in women of 40-49 years, so that at 60-69 years, 22.4% of women and 11.5% of men had gall stones or had undergone cholecystectomy. With the help of this study, best treatment option for cholecystectomy patient (whether to insert drain or not), was ensured in terms of post lap cholecystectomy collections of bile or blood, drain site pain. The study was provide knowledge whether drain insertion was benecial or harmful to patient. MATERIALS AND METHODS This comparative study was presented to the surgery OPD and emergency department with cholelithiasis within a period of 1 year from the approval of Research committee and Ethics Committee, Adesh institute of medical sciences and research, Bathinda. The primary outcome variable used to calculate sample size is amount of collection on post-operative day 3(Quantitative Variable) in both groups. Total 100 patients were present in this study. RESULT In this present study VAS median grade in patients with drain was G4 (48%), followed by G3(47%) then G2(5%). VAS median grade in without drain group was G2 (48%), followed by G3(31%) and G1(16%). CONCLUSION There is no signicant difference as far as post operative wound infection in laparoscopic Cholecystectomy with drain or without drain. Therefore in patients undergoing laparoscopic cholecystectomy keeping drain can be avoided as it does not provide any additional benet.

Sample Size for Survey Research: Review and Recommendations

Determining an appropriate sample size is vital in drawing realistic conclusions from research findings. Although there are several widely adopted rules of thumb to calculate sample size, researchers remain unclear about which one to consider when determining sample size in their respective studies. ‘How large should the sample be?’ is one the most frequently asked questions in survey research. The objective of this editorial is three-fold. First, we discuss the factors that influence sample size decisions. Second, we review existing rules of thumb related to the calculation of sample size. Third, we present the guidelines to perform power analysis using the G*Power programme. There is, however, a caveat: we urge researchers not to blindly follow these rules. Such rules or guidelines should be understood in their specific contexts and under the conditions in which they were prescribed. We hope that this editorial does not only provide researchers a fundamental understanding of sample size and its associated issues, but also facilitates their consideration of sample size determination in their own studies.

The development of a noninvasive behavioral test for assessment of goat–human interactions

The objective was to develop a protocol for a noninvasive behavioral test to assess goat–human interactions. Boer goats (n = 45) were housed in groups of 3 at the K-State Sheep and Meat Goat Unit. A 3-min human approach test (HAT) was conducted after their first- and seventh-handling experience (i.e., moved by group through the chute and scale). Video footage was analyzed using specialized software (Observer 11.5 XT, Leesburg, VA, USA). The ethogram included three main categories (mutually exclusive within category): 1) spatial (close, middle, far); 2) orientation (facing vs. turned-away); and 3) structural (lie, stand, and nutritive and nonnutritive oral behaviors). The durations of these behavior outcomes were converted to percent, and then applied to a weighted formula to create an approach index (AI). This index placed behaviors on a 0 to 100% scale (0 = lying in back of the pen; 100 = closest to the human, performing oral behaviors). The Proc Univariate (SAS v.9.3, SAS Inst. Inc., Cary, NC, USA) was used to evaluate descriptive statistics and calculate sample size for future projects from all behavior outcomes. In addition, each goat AI categorized as great approach (GA; ≥75% quartile), moderate approach (MA; 25% to 75% quartiles), or least approach (LA; ≤ 25%) and the 1st vs. 7th handling experience was evaluated using Chi-square (χ 2) analysis. The CV% was low for the AI outcome; therefore, less animals are needed if the AI is used rather than other behavior outcomes used. After the first-handling experience, there were less GA-goats than what was expected from the χ 2 analysis (χ 2 = 17.6; P ≤ 0.01; residual = 0.26; expected = 24.5). After the seventh-handling experience, there were more MA-goats than expected (χ 2 = 17.6; P ≤ 0.01; residual = 1.92; expected = 52.0). Repeated handling appeared to moderately improve goat approach to humans, which indicates that this protocol is a promising behavioral test to assess welfare of goats.

Effect size: a statistical basis for clinical practice

OBJECTIVE: Effect size (ES) is the statistical measure which quantifies the strength of a phenomenon and is commonly applied to observational and interventional studies. The aim of this review was to describe the conceptual basis of this measure, including its application, calculation and interpretation.RESULTS: As well as being used to detect the magnitude of the difference between groups, to verify the strength of association between predictor and outcome variables, to calculate sample size and power, ES is also used in meta-analysis. ES formulas can be divided into these categories: I – Difference between groups, II – Strength of association, III – Risk estimation, and IV – Multivariate data. The d value was originally considered small (0.20 > d ≤ 0.49), medium (0.50 > d≤ 0.79) or large (d ≥ 0.80); however, these cut-off limits are not consensual and could be contextualized according to a specific field of knowledge. In general, a larger score implies that a larger difference was detected.CONCLUSION: The ES report, in conjunction with the confidence interval and P value, aims to strengthen interpretation and prevent the misinterpretation of data, and thus leads to clinical decisions being based on scientific evidence studies.