***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Abstract Background . Incisional hernia is a common problem following  Roux-en-Y gastric bypass for morbid obesity . Patients and material s. We report the preliminary results of nonr and omized prophylactic use of polypropylene mesh in a group of 60 consecutive patients . The patients with highest body mass index , and /or history of abdominal hernias and profound liver damage had abdominal wall reinforced with mesh during an operation . A year later the wound was assessed in all patients . Results . In st and ard wound closure group ( n=48 ) incisional hernia was found in 9 cases ( 20 % ) . None of the patients with inserted mesh ( n=12 ) developed hernia . The length of hospital stay in mesh group was similar to that in the nonmesh group and shorter than in patients with hernia occurrence .  Mesh insertion was complicated with wound discharge in three patients . Conclusions . In our opinion prophylactic use of polypropylene mesh in bariatric patients is highly effective in postoperative hernia prevention Flaws in the design , conduct , analysis , and reporting of r and omised trials can cause the effect of an intervention to be underestimated or overestimated . The Cochrane Collaboration ’s tool for assessing risk of bias aims to make the process clearer and more Background / aim Incisional hernia is one of the major elements of morbidity after abdominal surgery , with high incidence in vertical midline abdominal incisions . However , the risk of developing an incisional hernia can be increased due to the patient ’s related factors ; therefore , more consideration has to be given to the choice of incision , wound closure and wound healing to protect against incisional hernia , especially in high-risk patients . In this study , we used prophylactic subfascial non-absorbable mesh reinforcement of midline closure in high-risk patients to detect whether fixing the wound with mesh is risky on a short-term basis and whether it is protective on a long-term basis . Patients and methods From October 2000 to December 2002 , 40 high-risk patients liable to develop postoperative incisional hernia underwent elective abdominal operations through midline abdominal incisions at the Department of Surgery , Gastroenterology and Laparoscopic Unit , Tanta University Hospital , Egypt . They were r and omly divided into two groups ; group A : patients for whom the  midline abdominal incisions were closed by conventional method and reinforced by subfascial polypropylene mesh ( 20 patients ) ; and group B : patients for whom the midline abdominal incisions were closed by conventional method only ( 20 patients ) with a follow up period of more than 20 months . Results There was no significant difference ( P = 0.075 ) in both groups regarding the age , sex and the average risk factor . Twenty-three patients ( 57.5 % ) presented with more than one risk factor ( 11 in group A and 12 in group B ) . The upper midline abdominal incisions were reported in 33 patients ( 19 upper and 14 extended upper ) . There was no significant difference between the overall local and systemic complications in both groups ( P = 0.4082 ) . However , the subcutaneous seroma and chronic wound pain were greater in patients with prophylactic mesh than those without mesh . One group A patients ( 5 % ) and three group B patients ( 15 % ) developed postoperative incisional hernia during the follow up period . Conclusion Prophylactic subfascial non-absorbable mesh reinforcement of midline closure in high-risk patients can be used safely and effectively to provide extrinsic strength of the wound without relying too much on the defective development of its own intrinsic strength and to prevent subsequent incisional hernia . There was no risk in the use of the mesh regarding local and systemic complication . However , the final statement should await the outcomes of the long-term follow up of the studied cases Objectives To evaluate the risk of bias tool , introduced by the Cochrane Collaboration for assessing the internal validity of r and omised trials , for inter-rater agreement , concurrent validity compared with the Jadad scale and Schulz approach to allocation concealment , and the relation between risk of bias and effect estimates . Design Cross sectional study . Study sample 163 trials in children . Main outcome measures Inter-rater agreement between review ers assessing trials using the risk of bias tool ( weighted κ ) , time to apply the risk of bias tool compared with other approaches to quality assessment ( paired t test ) , degree of correlation for overall risk compared with overall quality scores ( Kendall ’s τ statistic ) , and magnitude of effect estimates for studies classified as being at high , unclear , or low risk of bias ( metaregression ) . Results Inter-rater agreement on individual domains of the risk of bias tool ranged from slight ( κ=0.13 ) to substantial ( κ=0.74 ) . The mean time to complete the risk of bias tool was significantly longer than for the Jadad scale and Schulz approach , individually or combined ( 8.8 minutes ( SD 2.2 ) per study v 2.0 ( SD 0.8 ) , P<0.001 ) . There was low correlation between risk of bias overall compared with the Jadad scores ( P=0.395 ) and Schulz approach ( P=0.064 ) . Effect sizes differed between studies assessed as being at high or unclear risk of bias ( 0.52 ) compared with those at low risk ( 0.23 ) . Conclusions Inter-rater agreement varied across domains of the risk of bias tool . Generally , agreement was poorer for those items that required more judgment . There was low correlation between assessment s of overall risk of bias and two common approaches to quality assessment : the Jadad scale and Schulz approach to allocation concealment . Overall risk of bias as assessed by the risk of bias tool differentiated effect estimates , with more conservative estimates for studies at low risk Incisional hernia continues to be a serious postoperative complication in abdominal surgery . We present a prospect i ve r and omised study to evaluate the usefulness of placement of a supra-aponeurotic polypropylene mesh in the primary closure of laparotomies with a high risk of incisional hernia . Closure of a vertical laparotomy in 100 patients was accomplished with continuous suture using non-reabsorbable material , with placement of a polypropylene mesh on the aponeurotic surface in 50 patients . Three years after surgery , five patients in the group without the mesh had suffered incisional hernia . No incisional hernia was detected in the group in which closure was made using the mesh ( P=0.02 ) . Use of prosthetic material ( polypropylene mesh ) in the primary closure of laparotomies with a high risk of incisional hernia is useful for reduction of the rate of incisional hernias A quantitative systematic review , or meta- analysis , uses statistical methods to combine the results of multiple studies . Meta-analyses have been done for systematic review s of therapeutic trials , diagnostic test evaluations , and epidemiologic studies . Although the statistical methods involved may at first appear to be mathematically complex , their purpose is simple : They are trying to answer four basic questions . Are the results of the different studies similar ? To the extent that they are similar , what is the best overall estimate ? How precise and robust is this estimate ? Finally , can dissimilarities be explained ? This article provides some guidance in underst and ing the key technical aspects of the quantitative approach to these questions . We have avoided using equations and statistical notations ; interested readers will find implementations of the described methods in the listed references . We focus here on the quantitative synthesis of reports of r and omized , controlled , therapeutic trials because far more meta-analyses on therapeutic studies than on other types of studies have been published . For practical reasons , we present a stepwise description of the tasks that are performed when statistical methods are used to combine data . These tasks are 1 ) deciding whether to combine data and defining what to combine , 2 ) evaluating the statistical heterogeneity of the data , 3 ) estimating a common effect , 4 ) exploring and explaining heterogeneity , 5 ) assessing the potential for bias , and 6 ) presenting the results . Deciding Whether To Combine Data and Defining What To Combine By the time one performs a quantitative synthesis , certain decisions should already have been made about the formulation of the question and the selection of included studies . These topics were discussed in two previous articles in this series [ 1 , 2 ] . Statistical tests can not compensate for lack of common sense , clinical acumen , and biological plausibility in the design of the protocol of a meta- analysis . Thus , a reader of a systematic review should always address these issues before evaluating the statistical methods that have been used and the results that have been generated . Combining poor- quality data , overly biased data , or data that do not make sense can easily produce unreliable results . The data to be combined in a meta- analysis are usually either binary or continuous . Binary data involve a yes/no categorization ( for example , death or survival ) . Continuous data take a range of values ( for example , change in diastolic blood pressure after antihypertensive treatment , measured in mm Hg ) . When one is comparing groups of patients , binary data can be summarized by using several measures of treatment effect that were discussed earlier in this series [ 3 ] . These measures include the risk ratio ; the odds ratio ; the risk difference ; and , when study duration is important , the incidence rate . Another useful clinical measure , the number needed to treat ( NNT ) , is derived from the inverse of the risk difference [ 3 ] . Treatment effect measures , such as the risk ratio and the odds ratio , provide an estimate of the relative efficacy of an intervention , whereas the risk difference describes the intervention 's absolute benefit . The various measures of treatment effect offer complementary information , and all should be examined [ 4 ] . Continuous data can be summarized by the raw mean difference between the treatment and control groups when the treatment effect is measured on the same scale ( for example , diastolic blood pressure in mm Hg ) , by the st and ardized mean difference when different scales are used to measure the same treatment effect ( for example , different pain scales being combined ) , or by the correlation coefficients between two continuous variables [ 5 ] . The st and ardized mean difference , also called the effect size , is obtained by dividing the difference between the mean in the treatment group and the mean in the control group by the SD in the control group . Evaluating the Statistical Heterogeneity of the Data This step is intended to answer the question , Are the results of the different studies similar ( homogeneous ) ? It is important to answer this question before combining any data . To do this , one must calculate the magnitude of the statistical diversity ( heterogeneity ) of the treatment effect that exists among the different sets of data . Statistical diversity can be thought of as attributable to one or both of two causes . First , study results can differ because of r and om sampling error . Even if the true effect is the same in each study , the results of different studies would be expected to vary r and omly around the true common fixed effect . This diversity is called the within- study variance . Second , each study may have been drawn from a different population , depending on the particular patients chosen and the interventions and conditions unique to the study . Therefore , even if each study enrolled a large patient sample , the treatment effect would be expected to differ . These differences , called r and om effects , describe the between- study variation with regard to an overall mean of the effects of all of the studies that could be undertaken . The test most commonly used to assess the statistical significance of between- study heterogeneity is based on the chi-square distribution [ 6 ] . It provides a measure of the sum of the squared differences between the results observed and the results expected in each study , under the assumption that each study estimates the same common treatment effect . A large total deviation indicates that a single common treatment effect is unlikely . Any pooled estimate calculated must account for the between- study heterogeneity . In practice , this test has low sensitivity for detecting heterogeneity , and it has been suggested that a liberal significance level , such as 0.1 , should be used [ 6 ] . Estimating a Common Effect The questions that this step tries to answers are , 1 ) To the extent that data are similar , what is their best common point estimate of a therapeutic effect , and 2 ) how precise is this estimate ? The mathematical process involved in this step generally involves combining ( pooling ) the results of different studies into an overall estimate . Compared with the results of individual studies , pooled results can increase statistical power and lead to more precise estimates of treatment effect . Each study is given a weight according to the precision of its results . The rationale is that studies with narrow CIs should be weighted more heavily than studies with greater uncertainty . The precision is generally expressed by the inverse of the variance of the estimate of each study . The variance has two components : the variance of the individual study and the variance between different studies . When the between- study variance is found to be or assumed to be zero , each study is simply weighted by the inverse of its own variance , which is a function of the study size and the number of events in the study . This approach characterizes a fixed-effects model , as exemplified by the Mantel-Haenszel method [ 7 , 8 ] or the Peto method [ 9 ] for dichotomous data . The Peto method has been particularly popular in the past . It has the advantage of simple calculation ; however , although it is appropriate in most cases , it may introduce large biases if the data are unbalanced [ 10 , 11 ] . On the other h and , r and om-effects models also add the between- study variance to the within- study variance of each individual study when the pooled mean of the r and om effects is calculated . The r and om-effects model most commonly used for dichotomous data is the DerSimonian and Laird estimate of the between- study variance [ 12 ] . Fixed- and r and om-effects models for continuous data have also been described [ 13 ] . Pooled results are generally reported as a point estimate and CI , typically a 95 % CI . Other quantitative techniques for combining data , such as the Confidence Profile Method [ 14 ] , use Bayesian methods to calculate posterior probability distributions for effects of interest . Bayesian statistics are based on the principle that each observation or set of observations should be viewed in conjunction with a prior probability describing the prior knowledge about the phenomenon of interest [ 15 ] . The new observations alter this prior probability to generate a posterior probability . Traditional meta- analysis assumes that nothing is known about the magnitude of the treatment effect before r and omized trials are performed . In Bayesian terms , the prior probability distribution is noninformative . 
    Output:
    
Conclusion Mesh reinforcement of laparotomy significantly reduced the rate of incisional hernia  in high-risk patients .