***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: In 1990 , 215 patients with operable breast cancer were entered into a prospect i ve study of the prognostic significance of five biochemical markers and 15 other factors ( pathological/chronological/patient ) . After a median follow-up of 6.6 years , there were 77 recurrences and 77 deaths ( 59 breast cancer-related ) . By univariate analysis , patient outcome related significantly to 13 factors . By multivariate analysis , the most important of nine independent factors were : number of nodes involved , steroid receptors ( for oestrogen or progestogen ) , age , clinical or pathological tumour size and grade . Receptors and grade exerted their influence only in the first 3 years . Progestogen receptors ( immunohistochemical ) and oestrogen receptors ( biochemical ) were of similar prognostic significance . The two receptors were correlated ( r=+0.50 , P=0.001 ) and displaced each other from the analytical model but some evidence for the additivity of their prognostic values was seen when their levels were discordant 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 Objective Accurately predicting the prognosis of young patients with breast cancer ( < 40 years ) is uncertain since the literature suggests they have a higher mortality and that age is an independent risk factor . In this cohort study we considered two prognostic tools ; Nottingham Prognostic Index and Adjuvant Online ( Adjuvant ! ) , in a group of young patients , comparing their predicted prognosis with their actual survival . Setting North East Engl and Participants Data was prospect ively collected from the breast unit at a Hospital in Grimsby between January 1998 and December 2007 . A cohort of 102 young patients with primary breast cancer was identified and actual survival data was recorded . The Nottingham Prognostic Index and Adjuvant ! scores were calculated and used to estimate 10-year survival probabilities . Pearson 's correlation coefficient was used to demonstrate the association between the Nottingham Prognostic Index and Adjuvant ! scores . A constant yearly hazard rate was assumed to generate 10-year cumulative survival curves using the Nottingham Prognostic Index and Adjuvant ! predictions . Results Actual 10-year survival for the 92 patients who underwent potentially curative surgery for invasive cancer was 77.2 % ( CI 68.6 % to 85.8 % ) . There was no significant difference between the actual survival and the Nottingham Prognostic Index and Adjuvant ! 10-year estimated survival , which was 77.3 % ( CI 74.4 % to 80.2 % ) and 82.1 % ( CI 79.1 % to 85.1 % ) , respectively . The Nottingham Prognostic Index and Adjuvant ! results demonstrated strong correlation and both predicted cumulative survival curves accurately reflected the actual survival in young patients . Conclusions The Nottingham Prognostic Index and Adjuvant ! are widely used to predict survival in patients with breast cancer . In this study no statistically significant difference was shown between the predicted prognosis and actual survival of a group of young patients with breast cancer A prognostic index , previously derived in a group of 387 patients with primary breast cancer , has been recalculated for the same patients with over 5 years further follow-up and shown to be unchanged . The  prognostic index  has also been applied prospect ively to a further group of 320 patients and shown to be similarly effective in identifying patients with either a very good or a very poor prognosis . It has been verified that the index applies to patients with primary breast cancer . Patients have now been divided into 5 prognostic groups , predicting 11 % of patients with an almost normal survival and a further 10 % with a very poor prognosis . The index is used to stratify patients to study the effects of treatment regimes within groups of similar patients PURPOSE Our objective was to develop a nomogram to predict individual overall survival ( OS ) for primary breast cancer , based on pathological and biological tumor parameters . METHODS A retrospective study in a cohort of 180 patients with primary breast cancer was used to build the nomogram . Pathological factors and tumor proteases measured prospect ively in primary tumors were used . A multivariate Cox proportional hazards model was used to explore the relationship with OS , and regression coefficients were used to build the nomogram . The nomogram was internally vali date d with 200 bootstrap re- sample s. RESULTS The final variables included in the nomogram comprised tumor size ( P = 0.04 ) , nodal pathological status ( P = 0.01 ) , estrogen receptor status ( P = 0.04 ) , urokinase plasminogen activator inhibitor-1 ( PAI-1 ; P = 0.02 ) , thymidine kinase ( P = 0.03 ) , and cathepsin D ( P = 0.004 ) . The predictive accuracy of the nomogram at estimating the probability of OS , at both 2 and 5 years , was respectively 0.874 and 0.832 before and after calibration . CONCLUSION A nomogram to predict 2- and 5-year OS in BC , using histological and biological parameters was successfully developed . This prognostic tool should prove useful in decision-making and therapeutic research Abstract Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers . We vali date 
    Output:
    
Overall , the models performed well in the development cohorts but less accurately in some independent population s , particularly in patients with high risk and young and elderly patients .
An exception is the Nottingham Prognostic Index , which retains its predicting ability in most independent population s. Conclusions Many prognostic models have been developed for breast cancer , but only a few have been vali date d widely in different setting s. Importantly , their performance was suboptimal in independent population s , particularly in patients with high risk and in young and elderly patients