***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: PURPOSE Mutations affecting the KRAS gene are established predictive markers of outcome with anti-epithelial growth factor receptor ( EGFR ) antibodies in advanced colorectal cancer ( CRC ) . The relevance of these markers for anti-vascular endothelial growth factor ( VEGF ) therapy is controversial . This analysis was performed to assess the predictive and prognostic impact of KRAS and BRAF gene mutation status in patients receiving capecitabine with bevacizumab ( CG ) or capecitabine without bevacizumab in the phase III AGITG MAX ( Australasian Gastrointestinal Trials Group MAX ) study . PATIENTS AND METHODS Mutation status was determined for 315 ( 66.9 % ) of the original 471 patients . Mutation status was correlated with efficacy outcomes ( response rate , progression-free survival [ PFS ] , and overall survival [ OS ] ) , and a predictive analyses was undertaken . RESULTS Mutations in KRAS and BRAF genes were observed in 28.8 % and 10.6 % of patients , respectively . KRAS gene mutation status ( wild type [ WT ] v mutated [ MT ] ) had no prognostic impact for PFS ( hazard ratio [ HR ] , 0.89 ; CI , 0.69 to 1.14 ) or OS ( HR , 0.97 ; CI , 0.73 to 1.28 ) . BRAF mutation status ( WT v MT ) was not prognostic for PFS ( HR , 0.80 ; CI , 0.54 to 1.18 ) but was prognostic for OS ( HR , 0.49 ; CI , 0.33 to 0.73 ; P = .001 ) . By using the comparison of capecitabine versus capecitabine and bevacizumab ( CB ) and CB plus mitomycin ( CBM ) , KRAS gene mutation status was not predictive of the effectiveness of  bevacizumab for PFS or OS ( test for interaction P = .95 and 0.43 , respectively ) . Similarly , BRAF gene mutation status was not predictive of the effectiveness of bevacizumab for PFS or OS ( test for interaction P = .46 and 0.32 , respectively ) . CONCLUSION KRAS gene mutation status was neither prognostic for OS nor predictive of bevacizumab outcome in patients with advanced CRC . BRAF gene mutation status was prognostic for OS but was not predictive of outcome with bevacizumab PURPOSE To report the results of a planned safety analysis from a phase III trial comparing capecitabine plus oxaliplatin ( XELOX ) with bolus fluorouracil/leucovorin ( FU/LV ) as adjuvant therapy for stage III colon cancer . PATIENTS AND METHODS Patients with stage III colon carcinoma were r and omly assigned to receive either XELOX ( intravenous oxaliplatin plus oral capecitabine ; 3-week cycle for eight cycles ) or st and ard intravenous bolus FU/LV administered as the Mayo Clinic ( Mayo ; Rochester , MN ) or Roswell Park ( RP ; Buffalo , NY ) regimen for a similar length of time . A total of 1,886 patients were r and omly assigned . RESULTS The safety population comprised 1,864 patients , of whom 938 received XELOX and 926 received FU/LV . Most treatment-related adverse events ( AEs ) occurred at similar rates in both treatment arms . However , patients receiving XELOX experienced less all- grade diarrhea , alopecia , and more neurosensory toxicity , vomiting , and h and -foot syndrome than those patients receiving FU/LV . Compared with Mayo , XELOX showed fewer grade 3/4 hematologic AE and more grade 3/4 gastrointestinal AE . Compared with RP , XELOX showed less grade 3/4 gastrointestinal AE and more grade 3/4 hematologic AE . As expected grade 3/4 neurosensory toxicity and grade 3 h and -foot syndrome were higher with XELOX . Treatment-related mortality within 28 days from the last study dose was 0.6 % in the XELOX group and 0.6 % in the FU/LV group . CONCLUSION XELOX has a manageable tolerability profile in the adjuvant setting . Efficacy data will be available within the next 24 months Objective : The objective of this phase III trial was to compare chemotherapy combined with bevacizumab versus chemotherapy alone in the treatment of patients with advanced colorectal cancer . Methods : From September 2004 till September 2008 , 222 treatment-naive patients were enrolled and divided into 2 arms : 114 arm A patients were treated with leucovorin , 5-fluorouracil plus irinotecan in combination with bevacizumab , and 108 arm B patients were treated as above without bevacizumab .  All patients were stage IV with histologically confirmed adenocarcinoma . Results : The median overall survival of arm A patients was 22.0 months ( 95 % CI : 18.1–25.9 ) and 25.0 months ( CI : 18.1–31.9 ) for arm B patients . There was no statistically significant difference between the 2 arms ( p = 0.1391 ) . No statistically significant difference between the 2 arms regarding the response ratewas observed : partial response , 42 patients ( 36.8 % ) and 38 patients ( 35.2 % ) for arms A and B , respectively . Hematologic toxicity did not differ in the comparison of the 2 arms . Nonhematologic toxicity in arm A involved hypertension in 23 ( 20.2 % ) of the patients and proteinuria in 7 ( 6.1 % ) ; 3 patients experienced hemorrhage and 1 patient intestinal perforation . None of these side effects was observed in arm B patients . Conclusion : No statistically significant difference in median overall survival in patients with advanced colorectal cancer treated with bevacizumab plus a combination therapy ( arm A ) and those treated with the combination only , without bevacizumab ( arm B ) , was observed As professionals , we want to use the best treatments ; as patients , we want to be given them . Knowing whether an intervention works ( or does not work ) is fundamental to clinical decision making . However , clinical decision making involves more than simply taking published results of research directly to the bedside . Physicians need to consider how similar their patients are to those in the published studies , to take the values and preferences of their patients into account , and to consider their own experience with a given test or treatment . Evidence from clinical research is becoming increasingly important in medical- practice decisions as more and better evidence is published . But when is the evidence strong enough to justify changing a practice ? Individual studies that involve only small numbers of patients may have results that are distorted by the r and om play of chance and thus lead to less than optimal decisions . As is clear from other papers in this series , systematic review s identify , critically appraise , and review all the relevant studies on a clinical question and are more likely to give a valid answer . They use explicit methods and quality st and ards to reduce bias . Their results are the closest we can come to reaching the truth given our current state of knowledge . The questions about an intervention that a systematic review should answer are the following : 1 . Does it work ? 2 . If it works , how well does it work in general and compared with placebo , no treatment , or other interventions that are currently in use ? 3 . Is it safe ? 4 . Will it be safe and effective for my patients ? Whereas the critical appraisal and qualitative synthesis provided by review articles can be interpreted directly , the numerical products of quantitative review s can be more difficult to underst and and apply in daily clinical practice . This paper provides guidance on how to interpret the numerical and statistical results of systematic review s , translate these results into more underst and able terms , and apply them directly to individual patients . Many of these principles can also be used to interpret the numerical results of individual clinical studies . They are particularly relevant to systematic review s , however , because such review s contain more information than do primary studies and often exert greater influence than do individual studies . Making Sense of the Numerical Results of Clinical Studies Although the results of clinical studies can be expressed in intuitively meaningful ways , such results do not always easily translate into clinical decision making . For example , results are frequently expressed in terms of risk , which is an expression of the frequency of a given outcome . ( Risks are probabilities , which can vary between 0.0 and 1.0 . A probability of 0.0 means that the event will never happen , and a probability of 1.0 means that it always happens . ) Consider a hypothetical study of the recurrence of migraine headaches in a control group receiving placebo and a treatment group receiving a new antimigraine preparation , drug M ( a secondary prevention trial ) . Suppose that at the end of the trial , migraines recurred in 30 % of the control group ( the risk for recurrence was 0.30 ) but in only 5 % of the drug M group ( risk of 0.05 ) ( Table 1 ) . Table 1 . Numerical Expression of Hypothetical Clinical Trial Results The outcomes of the study are clear enough for the two groups when they are examined separately . But clinicians and patients are more interested in the comparative results , that is , the outcome in one group relative to the outcome in the other group . This overall ( comparative ) result can be expressed in various ways . For example , the relative risk , which is the risk in the treatment group relative to that in the control group , is simply the ratio of the risks in the two groups . In other words , relative risk is the risk in the treatment group divided by that in the control group , 0.05 0.30 , or 0.17 . The comparison can also be expressed as the reduction in relative risk , which is the ratio between the decrease in risk ( in the treatment group ) and the risk in the control group , 0.25 0.30 , or 0.83 ( Table 1 ) . ( The relative risk reduction can also be calculated as 1 relative risk ) . Although the clinical meaning of relative risk ( and relative risk reduction ) is reasonably clear , relative risk has the distinct disadvantage that a given value ( for example , 0.17 ) is the same whether the risk with treatment decreases from 0.80 to 0.14 , from 0.30 to 0.05 , from 0.001 to 0.00017 , and so forth . The clinical implication s of these changes clearly differ from one another enormously and depend on the specific disease and intervention . An important alternate expression of comparative results , therefore , is the absolute risk reduction . Absolute risk reduction is determined by subtracting the risk in one group from the risk in the other ( for example , the risk in the treatment group is subtracted from the risk in the placebo group ) . In the case of our migraine study , the absolute risk reduction would be 0.30 0.05 , which equals 0.25 , or 25 percentage points . In contrast , for a study in which the risk decreased from 0.001 to 0.00017 , the absolute risk reduction would be only 0.00083 , or 0.083 percentage points , which is a trivial change in comparison ( Table 1 ) . This arithmetic emphasizes the difficulty of expressing the results of clinical studies in meaningful ways . Relative risk and relative risk reduction clearly give a quantitative sense of the effects of an intervention in proportional terms but provide no clue about the size of an effect on an absolute scale . In contrast , although it tells less about proportional effects , absolute risk says a great deal about whether an effect is likely to be clinical ly meaningful . Despite this benefit , even absolute risk is problematic because it is a dimensionless , abstract number ; that is , it lacks a direct connection with the clinical situation in which the patient and physician exist . However , another way of expressing clinical research results can provide that clinical link : the number needed to treat ( NNT ) . Number Needed To Treat The NNT for a given therapy is simply the reciprocal of the absolute risk reduction for that treatment [ 1 , 2 ] . In the case of our hypothetical migraine study ( in which risk decreased from 0.30 without treatment with drug M to 0.05 with treatment with drug M , for a relative risk of 0.17 , a relative risk reduction of 0.83 , and an absolute risk reduction of 0.25 ) , the NNT would be 1 0.25 , or 4 . In concrete clinical terms , an NNT of 4 means that you would need to treat four patients with drug M to prevent migraine from recurring in one patient . To emphasize the difference between the concepts embodied in NNT and relative risk , recall the various situations mentioned above , in all of which the relative risk was 0.17 but in which the absolute risk decreased from 0.80 to 0.14 in one case and from 0.001 to 0.00017 in another . Note that the corresponding NNTs in these two other cases are 1.5 and 1204 , respectively : that is , you would need to treat 1.5 and 1204 patients to obtain a therapeutic result in these two situations compared with 4 patients with drug M ( Table 1 ) . The NNT can be calculated easily and kept as a single numerical reminder of the effectiveness ( or , as we will see , the potential for harm ) of a particular therapy . As we suggested , the NNT has the crucial advantage of direct applicability to clinical practice because it shows the effort that is required to achieve a particular therapeutic target . The NNT has the additional advantage that it can be applied to any beneficial outcome or any adverse event ( when it becomes the number needed to harm [ NNH ] ) . The concept of NNT always refers to a comparison group ( in which patients receive placebo , no treatment , or some other treatment ) , a particular treatment outcome , and a defined period of treatment . In other words , the NNT is the number of patients that you will need to treat with drug or treatment A to achieve an improvement in 
    Output:
    
Conclusion The combination of chemotherapy with bevacizumab  increased the response rate , progression-free survival and overall survival of patients with mCRC without prior chemotherapy .
The results of progression-free survival ( PFS ) and overall survival ( OS ) were comparatively higher in those subgroups of patients receiving bolus 5-FU or capecitabine-based chemotherapy plus bevacizumab , when compared to patients treated with infusional % -FU plus bevacizumab ( no difference in PFS and OS ) .
Regarding the type of cytotoxic scheme , regimens containing irinotecan and fluoropyrimidine monotherapy showed superior efficacy results when combined to bevacizumab