***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: Objectives To determine the maximal and safely dose of preoperative radiotherapy and concurrently intensified chemotherapy regimen ( raltitrexed plus oxaliplatin ) in locally advanced rectal cancer patients . Methods Patients with cT3-T4 and /or cN≥1 or locally recurrent rectal cancer were sequentially assigned to 4 treatment schedules of chemoradiation : st and ard radiotherapy ( 50.4 Gy/5.5 wk ) plus raltitrexed ( cohort A ) , accelerated radiotherapy ( 55 Gy/5 wk ) plus raltitrexed ( cohort B ) , st and ard radiotherapy plus raltitrexed and oxaliplatin ( cohort C ) , accelerated radiotherapy plus raltitrexed and oxaliplatin ( cohort D ) . Patients were treated in cohorts of 6 to 12 per group . The maximal tolerated dose was exceeded if more than one-third of patients in a given cohort experienced dose-limiting toxicity ( DLT ) . DLT was defined as any grade ≥3 toxicity according to the Radiation Therapy Oncology Group criteria . Results Forty-six consecutive patients were enrolled . In cohort A , 6 patients received the planned treatment with no DLT . In cohort B , 1 of 8 patients experienced a DLT . In cohort C , a DLT occurred in 2 of 6 patients and therefore , a cohort expansion was required . Three of 16 patients treated at this dose level experienced a DLT . In addition , cohort D was exp and ed and DLT was found in 4 of 16 patients . Therefore , the maximal tolerated dose was not exceeded at any treatment level . Conclusions An intensified regimen of chemoradiotherapy delivering raltitrexed and oxaliplatin concurrently with concomitant boost radiotherapy ( 55 Gy/5 wk ) can be safely administered in patients with locally advanced rectal cancer . On the basis of these results , this intensified regimen could be tested in a phase II study Abstract Objective To assess the accuracy of preoperative staging of rectal cancer with magnetic resonance imaging to predict surgical circumferential resection margins . Design Prospect i ve observational study of rectal cancers treated by colorectal multidisciplinary teams between January 2002 and October 2003 . Setting 11 colorectal units in four European countries . Participants 408 consecutive patients presenting with all stages of rectal cancer and undergoing magnetic resonance imaging before total mesorectal excision surgery and histopathological assessment of the surgical specimen . Main outcome measures Accuracy of magnetic resonance imaging in predicting a curative resection based on the histological yardstick of presence or absence of tumour at the margins of the specimen . Results 354 of the 408 patients had a clear circumferential resection margin ( 87 % , 95 % confidence interval 83 % to 90 % ) . Specificity for prediction of a clear margin by magnetic resonance imaging was 92 % ( 327/354 , 90 % to 95 % ) . High resolution scans were technically satisfactory in 93 % ( 379/408 ) . Surgical specimens were histopathologically grade d as complete or moderate in 80 % ( 328/408 ) , and the median lymph node harvest was 12 ( range 0 - 49 ) . Magnetic resonance imaging predicted clear margins in 349 patients . At surgery 327 had clear margins ( 94 % , 91 % to 96 % ) . Conclusion High resolution magnetic resonance imaging accurately predicts whether the surgical resection margins will be clear or affected by tumour . This technique can be reproduced accurately in multiple centres to predict curative resection and warns the multidisciplinary team of potential failure of surgery , thus enabling selection of patients for preoperative treatment PURPOSE Intergroup Study 0114 was design ed to study the effect of various chemotherapy regimens delivered after potentially curative surgical resection of T3 , T4 , and /or node-positive rectal cancer . A subset analysis was undertaken to investigate the prevalence and influence of salvage therapy among patients with recurrent disease . PATIENTS AND METHODS Adjuvant therapy consisted of two cycles of fluorouracil (FU)-based chemotherapy followed by pelvic irradiation with chemotherapy and two more cycles of chemotherapy after radiation therapy . A total of 1,792 patients were entered onto the study and 1,696 were assessable . After a median of 8.9 years of follow-up , 715 patients ( 42 % ) had disease recurrence , and an additional 10 % died without evidence of disease . Five hundred patients with follow-up information available had a single organ or single site of first recurrence ( 73.5 % of all recurrences ) . RESULTS A total of 171 patients ( 34 % of those with a single organ or single site of recurrence ) had a potentially curative resection of the metastatic or locally recurrent disease . Single-site first recurrences in the liver , lung , or pelvis occurred in 448 patients ( 90 % of the single-site recurrences ) , with 159 ( 35 % ) of these undergoing surgical resection for attempted cure . Overall survival differed significantly between the resected and nonresected groups ( P < .0001 ) , with overall 5-year probabilities of.27 and .06 , respectively . Controlling for worst performance status at the time of recurrence does not alter this relationship . Patients who underwent salvage surgery had significantly increased survival ( P < .001 ) for each site . CONCLUSION Attempted surgical salvage of rectal cancer recurrence is performed commonly in the United States . The chance of a long-term cure with such intervention is approximately 27 % Background Care of patients with locally recurrent rectal cancer ( LRRC ) requires careful patient selection . While curative resection offers survival benefits , significant trade-offs exist for the patient . Knowledge of patient-reported outcomes will help inform treatment decisions . Methods Quality of life ( QOL ) and pain were prospect ively assessed in 105 patients treated for LRRC at a single institution , using the vali date d Functional Assessment of Cancer Therapy-Colorectal  ( FACT-C ) and  Brief Pain Inventory ( BPI ) question naires . In 54 patients enrolled and followed from diagnosis of LRRC , relationship between pretreatment pain , QOL , and overall survival ( OS ) were examined . Results Patients underwent curative surgical resection ( C , 59 % ) , noncurative surgery ( NC , 12 % ) or nonsurgical treatment ( NS , 28 % ) . Median OS was 7.1 , 1.4 , and 1.9 years , respectively ( C versus NC : p < 0.001 ; C versus NS : p = 0.006 ; NC versus NS : p = 0.261 ) . Physical well-being QOL differed over time ( p = 0.042 ) , with greatest difference between C and NC surgery patients ( p = 0.049 ) . The remaining QOL domain scores and pain scores demonstrated no significant time or treatment effect . For the 54 patients assessed from diagnosis , median OS was independently predicted by treatment group ( C , NC , NS : 4.3 , 1.7 , versus 2.4 years ; p < 0.001 ) and pretreatment pain intensity ( score ≤ 4 versus > 4 : 3.8 versus 2.0 years ; p = 0.001 ) . Conclusion Curative surgery offered prolonged survival , but significant pain exists among long-term survivors and should be a focus of survivorship care . Noncurative surgery did not offer apparent advantages over nonsurgical palliation . Patient ’s pretreatment pain has prognostic value , and should be assessed , treated , and considered in treatment decisions 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 
    Output:
    
Patients undergoing R0 resection have the greatest survival advantage following surgery for recurrent rectal cancer .
There is a survival advantage for R1 over R2 resection , but there may be no benefit of R2 resection over palliative treatment