***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: Prognosis studies are investigations of future events or the evaluation of associations between risk factors and health outcomes in population s of patients ( 1 ) . The results of such studies improve our underst and ing of the clinical course of a disease and assist clinicians in making informed decisions about how best to manage patients . Prognostic research also informs the design of intervention studies by helping define subgroups of patients who may benefit from a new treatment and by providing necessary information about the natural history of a disorder ( 2 ) . There has recently been a rapid increase in the use of systematic review methods to synthesize the evidence on research questions related to prognosis . It is essential that investigators conducting systematic review s thoroughly appraise the method ologic quality of included studies to be confident that a study 's design , conduct , analysis , and interpretation have adequately reduced the opportunity for bias ( 3 , 4 ) . Caution is warranted , however , because inclusion of method ologically weak studies can threaten the internal validity of a systematic review ( 4 ) . This follows abundant empirical evidence that inadequate attention to biases can cause invalid results and inferences ( 5 - 9 ) . However , there is limited consensus on how to appraise the quality of prognosis studies ( 1 ) . A useful framework to assess bias in such studies follows the basic principles of epidemiologic research ( 10 , 11 ) . We focus on 6 areas of potential bias : study participation , study attrition , prognostic factor measurement , confounding measurement and account , outcome measurement , and analysis . The main objectives of our review of review s are to describe methods used to assess the quality of prognosis studies and to describe how well current practice s assess potential biases . Our secondary objective is to develop recommendations to guide future quality appraisal , both within single studies of prognostic factors and within systematic review s of the evidence . We hope this work facilitates future discussion and research on biases in prognosis studies and systematic review s. Methods Literature Search and Study Selection We identified systematic review s of prognosis studies by search ing MEDLINE ( 1966 to October 2005 ) using the search strategy recommended by McKibbon and colleagues ( 12 ) . This strategy combines broad search terms for systematic review s ( systematic review .mp ; meta- analysis .mp ) and a sensitive search strategy for prognosis studies ( cohort , incidence , mortality , follow-up studies , prognos * , predict * , or course ) . We also search ed the reference lists of included review s and method ologic papers to identify other relevant publications . We restricted our search to English- language publications . One review er conducted the search and selected the studies . Systematic review s , defined as review s of published studies with a comprehensive search and systematic selection , were included if they assessed the method ologic quality of the included studies by using 1 or more explicit criteria . We excluded studies if they were meta-analyses of independent patient data only , if their primary goal was to investigate the effectiveness of an intervention or specific diagnostic or screening tests , or if they included studies that were not done on humans . Data Extraction and Synthesis Individual items included in the quality assessment of the systematic review s were recorded as they were reported in the publication ( that is , the information that would be available to readers and future review ers ) . We review ed journal Web sites and contacted the authors of the systematic review s for additional information when authors made such an offer in their original papers . When review s assessed different study design s by using different sets of quality items , we extracted only those items used to assess cohort studies . We constructed a comprehensive list of distinct items that the review s used to assess the quality of their included studies . The full text of each review was screened . All items used by the review authors to assess the quality of studies were extracted into a computerized spreadsheet by 1 review er . Two experienced review ers , a clinical epidemiologist and an epidemiologist , independently synthesized the quality items extracted from the prognosis review s to determine how well the systematic review s assessed potential biases . We did this in 3 steps : 1 ) identified distinct concepts or domains addressed by the quality items ; 2 ) grouped each extracted quality item into the appropriate domain or domains ; and 3 ) identified the domains necessary to assess potential biases in prognosis studies . We then used this information to assess how well the review s ' quality assessment included items from the domains necessary to assess potential biases . After completing each of the first 3 steps , the review ers met to attempt to reach a consensus . The consensus process involved each review er presenting his or her observations and results , followed by discussion and debate . A third review er was available in cases of persistent disagreement or uncertainty . In the first step , all domains addressed by the quality items were identified . The first review er iteratively and progressively defined the domains as items were extracted from the included review s. The second review er defined domains from a r and om list of all extracted quality items . Limited guidance was provided to the review ers so that their assessment s and definitions of domains would be independent . The review ers agreed on a final set of domains that adequately and completely defined all of the extracted items . In the second step , review ers independently grouped each extracted item into the appropriate domains . Review ers considered each extracted item by asking , What is each particular quality item addressing ? or What are the review 's authors getting at with the particular quality assessment item ? . Items were grouped into the domain or domains that best represented the concepts being addressed . For example , the extracted items at least 80 % of the group originally identified was located for follow-up and follow-up was sufficiently complete or does n't jeopardize validity were each independently classified by both review ers as assessing the domain completeness of follow-up adequate , whereas the extracted item quantification and description of all subjects lost to follow-up was classified as assessing the domain completeness of follow-up described . In the third step , we identified the domains necessary to assess potential biases . Each review er considered the ability of the identified domains to adequately address , at least in part , 1 of the following 6 potential biases : 1 ) study participation , 2 ) study attrition , 3 ) prognostic factor measurement , 4 ) confounding measurement and account , 5 ) outcome measurement , and 6 ) analysis . Domains were considered to adequately address part of the framework if information garnered from that domain would inform the assessment of potential bias . For example , both review ers judged that the identified domain study population represents source population or population of interest assessed potential bias in a prognosis study , whereas the domain research question definition did not , although the latter is an important consideration in assessing the inclusion of studies in a systematic review . Finally , on the basis of our previous ratings , we looked at whether each review included items from the domains necessary to assess the 6 potential biases . We calculated the frequency of systematic review s by assessing each potential bias and the number of review s that adequately assessed bias overall . From this systematic synthesis , we developed recommendations for improving quality appraisal in future systematic review s of prognosis studies . We used Microsoft Access and Excel 2002 ( Microsoft Corp. , Redmond , Washington ) for data management and SAS for Windows , version 9.1 ( SAS Institute , Inc. , Cary , North Carolina ) for descriptive statistics . Role of the Funding Sources The funding sources , the Canadian Institutes of Health Research , the Canadian Chiropractic Research Foundation , the Ontario Chiropractic Association , and the Ontario Ministry of Health and Long Term Care , did not have a role in the collection , analysis , or interpretation of the data or in the decision to su bmi t the manuscript for publication . Results We identified 1384 potentially relevant articles . Figure 1 shows a flow chart of studies that were included and excluded . Figure 2 shows the number of review s identified by year of publication . We excluded 131 systematic review s of prognosis studies that did not seem to include any quality assessment of the included studies ; this represented 44 % of prognosis review s. We included 163 review s of prognosis studies in our analysis ( 13 - 175 ) . The most common topics were cancer ( 15 % ) , musculoskeletal disorders and rheumatology ( 13 % ) , cardiovascular ( 10 % ) , neurology ( 10 % ) , and obstetrics ( 10 % ) . Other review s included a wide range of health and health care topics . Sixty-three percent of the review s investigated the association between a specific prognostic factor and a particular outcome ; the remainder investigated multiple prognostic factors or models . The number of primary studies included in each systematic review ranged from 3 to 167 ( median , 18 [ interquartile range , 12 to 31 ] ) . A complete description of the included review s is available from the authors on request . Figure 1 . Flow diagram of inclusion and exclusion criteria of systematic review s. Figure 2 . Number of systematic review s of prognosis studies identified over time . Quality Items One hundred fifty-three review s provided adequate detail to allow extraction of quality items . Eight hundred eighty-two distinct quality items were extracted from the review s. Most review s developed their own set of quality items , with only a few applying criteria from previous review s. Most quality items BACKGROUND The superiority of cisplatin and gemcitabine ( CisGem ) chemotherapy over gemcitabine ( Gem ) alone in patients with advanced biliary tract cancer ( ABC ) has been demonstrated in two r and omised trials ; ABC02 and the Biliary Tract ( BT ) 22 study . We used a combined data set from these two trials to investigate the derived neutrophil-to-lymphocyte ratio ( dNLR ) , which is thought to be a prognostic factor associated with clinical outcomes in several solid tumours , including ABC . METHODS  White blood cell ( WBC ) and absolute neutrophil count ( ANC ) were available for 379 of 410 patients from ABC-02 and all 83 patients in BT-22 . The dNLR was calculated as ANC/(WBC-ANC ) , as previously specified . We examined the association between dNLR and overall survival ( OS ) and progression-free survival ( PFS ) , as well as comparing the treatment effect in two patient groups defined by their dNLR level . A high dNLR was defined as ≥3.0 , which was approximately the upper tertile value . RESULTS A total of 462 individual patient records were analysed , 328 with baseline dNLR <3 and 134 with dNLR ≥3 . There were 443 deaths in the cohort , and all surviving patients had a dNLR <3 . There was strong evidence that dNLR was closely associated with both OS [ hazard ratio ( HR ) , 1.62 ; 95 % confidence interval ( CI ) 1.32 - 2.01 ] and PFS ( HR , 1.40 ; 95 % CI 1.13 - 1.72 ) . There was limited evidence ( P = 0.10 ) of a differential effect of CisGem on OS between the two dNLR groups , but this was clearest in the ABC-02 data set ( P = 0.06 ) . There was good evidence ( P = 0.008 ) of an association between low baseline dNLR and long-term survival  on a CisGem regimen . There was also good evidence of an association between ECOG performance status ( split at 0 and 1 versus 2 ) on both OS ( P < 0.001 ) and PFS ( P = 0.01 ) , but no evidence of a differential treatment effect , with both groups receiving benefit from the addition of cisplatin . CONCLUSIONS These data confirm that high dNLR is associated with worse OS and PFS , and suggests it may also be predictive of benefit for the addition of cisplatin to gemcitabine in European patients with ABC . Incorporating dNLR into the clinical context may better inform prognosis and chemotherapy decisions in ABC patients Background : The host inflammatory response has a vital role in carcinogenesis and tumour progression . We examined the prognostic value of  inflammatory markers ( albumin , white-cell count and its components , and platelets )  in pre-treated patients with advanced renal cell carcinoma ( RCC ) . Methods : Using data from a r and omised trial , multivariable proportional hazards models were generated to examine the impact of inflammatory markers and established prognostic factors ( performance status , calcium , and haemoglobin ) on overall survival ( OS ) . We evaluated a new prognostic classification incorporating additional information from inflammatory markers . Results : Of the 416 patients , 362 were included in the analysis .  Elevated neutrophil counts , elevated platelet counts , and a high neutrophil – lymphocyte ratio were significant independent predictors for shorter OS in a model with established prognostic factors . The addition of inflammatory markers improves the discriminatory value of the prognostic classification as compared with established factors alone ( C-statistic 0.673 vs 0.654 , P=0.002 for the difference ) , with 25.8 % ( P=0.004 ) of patients more appropriately classified using the new classification . Conclusion : Markers of systemic inflammation contribute significantly to prognostic classification in addition to established factors for pre-treated patients with advanced RCC . Upon validation of these data in independent studies , stratification of patients using these markers in future clinical trials is recommended Objective : In cancer patients , the balance between neutrophil ( N ) and lymphocyte ( L ) cell counts fluctuates with advancing disease . The objective of our study was to determine the prognostic implication s of the N/L ratio in the peripheral blood of gastric cancer patients . Methods : Study participants were identified from a prospect i ve cohort of patients with advanced gastric cancer in Japan ( n = 1,220 ) . Results : The median baseline N/L was 2.58 ( range , 0.63–12.7 ) . Univariate analysis revealed that patients with an N/L ≧2.5 ( n = 644 ) had a significantly poorer prognosis than those with an N/L < 2.5 ( n = 576 ; log rank test , p = 0.019 × 10–12 ) . The median survival times for these two groups were 239 ( 95 % confidence interval , CI , 217–251 days ) and 363 days ( 95 % CI , 334–406 days ) , respectively , while the 1-year survival rates were 30 ( 95 % CI , 26– 
    Output:
    
Overall , high pretreatment blood NLR could be an adverse prognostic indicator for advanced tumor .