***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: Background The objective of this study was to evaluate the efficacy and toxicity of infusional 5-fluorouracil ( 5-FU ) , folinic acid and oxaliplatin ( modified FOLFOX-6 ) in patients with advanced gastric cancer ( AGC ) , as first-line palliative combination chemotherapy . We also analyzed the predictive or prognostic value of germline polymorphisms of c and i date genes associated with 5-FU and oxaliplatin . Methods Seventy-three patients were administered a 2 hour infusion of oxaliplatin ( 100 mg/m2 ) and folinic acid ( 100 mg/m2 ) followed by a 46 hour continuous infusion of 5-FU ( 2,400 mg/m2 ) . Genomic DNA from the patients ' peripheral blood mononuclear cells was extracted . Ten polymorphisms within five genes were investigated including TS , GSTP , ERCC , XPD and XRCC . Results The overall response rate ( RR ) was 43.8 % . Median time to progression ( TTP ) and overall survival ( OS ) were 6.0 months and 12.6 months , respectively . Toxicities were generally tolerable and manageable . The RR was significantly higher in patients with a 6-bp deletion homozygote ( -6 bp/-6 bp ) in TS-3'UTR ( 55.0 % vs. 30.3 % in + 6 bp/+6 bp or + 6 bp/-6 bp , p = 0.034 ) , and C/A or A/A in XPD156 ( 52.0 % vs. 26.1 % in C/C , p = 0.038 ) . The -6 bp/-6 bp in TS-3'UTR was significantly associated with a prolonged TTP and OS . In a multivariate analysis , the 6-bp deletion in TS-3'UTR was identified as an independent prognostic marker of TTP ( hazard ratio = 0.561 , p = 0.032 ) . Conclusion Modified FOLFOX-6 chemotherapy appears to be active and well tolerated as first line chemotherapy in AGC patients . The 6-bp deletion in TS-3'UTR might be a c and i date to select patients who are likely to benefit from 5-FU based modified FOLFOX-6 in future large scale trial Purpose The aim of this study was to assess whether genetic polymorphisms in p53 , glutathione S-transferase P1 ( GSTP1 ) , GSTM1 , excision repair cross complementing group 1 ( ERCC1 ) and X-ray repair cross-complementing group 1 ( XRCC1 ) genes are associated with clinical outcome of gastric cancer patients treated with oxaliplatin-based adjuvant chemotherapy .  Methods The genetic polymorphisms in p53 , GSTP1,GSTM1 ( null ) , ERCC1 and XRCC1 were determined in 102 gastric cancer patients treated with oxaliplatin-based adjuvant chemotherapy using polymerase chain reaction-ligation detection reaction method .  Results Among the five studied polymorphisms , p53 codon 72 Pro/Pro , GSTP1 codon 105 Ile/Ile , and XRCC1 codon 399 Gln/Gln + Arg/Gln were associated with poor relapse-free survival and overall survival ( P < 0.05 ) ; and the prognostic effect was retained in the Cox multivariate analysis . Combination analysis with the three polymorphisms using the Kaplan – Meier method and Cox multivariate analysis revealed that the relapse-free and overall survivals significantly increase with the number of favorable genotypes ( P < 0.05 ) . No significant association was found between the GSTM1 ( null ) or the ERCC1 codon 118 genotypes and the clinical outcome ( P > 0.05 ) . Conclusion Testing for p53 Arg72Pro , GSTP1 Ile105Val , and XRCC1 Arg399Gln polymorphisms may allow identification of gastric cancer patients who will benefit from oxaliplatin-based adjuvant chemotherapy . Selecting specific adjuvant treatments according to the individual genetic background may represent an innovative strategy that warrants prospect i ve studies 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 . Bayesian approaches may also allow the incorporation of indirect evidence in generating prior distributions [ 14 ] and may be particularly helpful in situations in which few data from r and omized studies exist [ 16 ] . Bayesian analyses may also be used to account for the uncertainty introduced by estimating the between- study variance in the r and om-effects model , leading to more appropriate estimates and predictions of treatment efficacy [ 17 ] . Exploring and Explaining Heterogeneity The next important issue is whether the common estimate obtained in the previous step is robust . Sensitivity analyses determine whether the common estimate is influenced by changes in the assumptions and in the protocol for combining the data . A comparison of the results of fixed- and r and om-effects models is one such sensitivity analysis [ 18 ] . Generally , the r and om-effects model produces wider CIs than does the fixed-effects model , and the level of statistical significance may therefore be different depending on the model used . The pooled point estimate per se is less likely to be affected , although exceptions are possible [ 19 ] . Other sensitivity analyses may include the examination of the residuals and the chi-square components [ 13 ] and assessment of the effect of deleting each study in turn . Statistically significant results that depend on a single study may require further exploration . Cumulative Meta- Analysis PURPOSE To investigate whether polymorphisms with putative influence on fluorouracil/cisplatin activity are associated with clinical outcomes of patients with advanced gastric cancer ( AGC ) . PATIENTS AND METHODS Peripheral blood sample s from 175 prospect ively enrolled AGC patients treated with fluorouracil/cisplatin palliative chemotherapy  were used for genotyping 13 polymorphisms in nine genes ( TS , MTHFR , XPD , ERCC1 , XRCC1 , XRCC3 , GSTPI , GSTTI , GSTMI ) . Genotypes were correlated to response and survival . RESULTS The overall response rate was 41 % , the median progression-free survival ( PFS ) was 24 weeks ( range , 4 to 50 weeks ) , and the median overall survival ( OS ) was 39 weeks ( range , 8 to 72 + weeks ) . Chemoresistance and poor survival were significantly associated with TS 5'-UTR 3G-genotype ( 2R/3 G , 3C/3 G , 3G/3 G ) and GSTP1 105 A/A homozygous genotype 
    Output:
    
No significant associations were observed between XRCC1 Arg194Trp and objective response .
The data suggest that the XRCC1 Arg399Gln polymorphism may be a prognostic biomarker of OS for platinum-based gastric cancer treatment .