***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 Severity scores are useful to guarantee similar disease severity among groups in clinical trials and to enable comparison between different studies . The aim of this study was to assess the performance of the third generation models of severity scoring systems [ simplified acute physiology score ( SAPS ) 3 , acute physiology and chronic health evaluation ( APACHE ) IV and mortality probability model (MPM)-III ] in acute kidney injury ( AKI ) patients in the intensive care unit ( ICU ) . METHODS Three hundred and sixty-six consecutive AKI critically ill patients were prospect ively assessed in six ICUs of an academic tertiary care center . Scores were applied on AKI diagnosis day ( DD ) and on the day of nephrology consultation ( NCD ) . Discrimination was assessed by area under the receiver operating characteristic curve ( AUCROC ) and calibration by Hosmer-Lemeshow ( HL ) goodness-of-fit test . RESULTS Hospital mortality rate was 67.8 % . SAPS 3 general and Central and South America ( CSA ) customized equations presented identical good discrimination ( AUCROC curve : 0.80 on NCD ) and satisfactory HL tests on both analyzed days ( P > 0.100 ) . CSA SAPS 3 equation predicted mortality more accurately [ st and ardized mortality ratio ( SMR ) on NCD = 1.00 ( 95 % confidence interval ( CI ) 0.84 - 1.34 ) ] . APACHE IV and MPM-III scores presented similar discrimination compared to SAPS 3 on both analyzed days ( P > 0.05 ) . APACHE IV presented satisfactory HL tests over time ( P > 0.100 ) but underestimated mortality [ SMR on DD = 1.92 ( 95 % CI 1.61 - 2.23 ) ; SMR on NCD = 1.46 ( 95 % CI 1.48 - 1.96 ) ] . MPM-III showed unsatisfactory HL test results ( P = 0.027 on DD ; P = 0.045 on NCD ) and underestimated mortality [ SMR on NCD = 2.09 ( 95 % CI 1.48 - 1.96 ) ] . CONCLUSIONS SAPS 3 , especially the geographical customized equation , presented good discrimination and calibration performances , accurately predicting mortality in this group of AKI critically ill patients BACKGROUND The Simplified Acute Physiology Score ( SAPS ) 3 has recently been developed , but not yet vali date d in surgical intensive care unit ( ICU ) patients . We compared the performance of SAPS 3 with SAPS II and the Acute Physiology and Chronic Health Evaluation ( APACHE ) II score in surgical ICU patients . METHODS Prospect ively collected data from all patients admitted to a German university hospital postoperative ICU between August 2004 and December 2005 were analysed . The probability of ICU mortality was calculated for SAPS II , APACHE II , adjusted APACHE II ( adj-APACHE II ) , SAPS 3 , and SAPS 3 customized for Europe [ C-SAPS3 ( Eu ) ] using st and ard formulas . To improve calibration of the prognostic models , a first-level customization was performed , using logistic regression on the original scores , and the corresponding probability of ICU death was calculated for the customized scores ( C-SAPS II , C-SAPS 3 , and C-APACHE II ) . RESULTS The study included 1851 patients . Hospital mortality was 9 % .  Hosmer and Lemeshow statistics showed poor calibration for SAPS II , APACHE II , adj-APACHE II , SAPS 3 , and C-SAPS 3 ( Eu ) , but good calibration for C-SAPS II , C-APACHE II , and C-SAPS 3 . Discrimination was generally good for all models [ area under the receiver operating characteristic curve ranged from 0.78 ( C-APACHE II ) to 0.89 ( C-SAPS 3 ) ] . The  C-SAPS 3  score appeared to have the best calibration curve on visual inspection . CONCLUSIONS In this group of surgical ICU patients , the performance of SAPS 3 was similar to that of APACHE II and SAPS II . Customization improved the calibration of all prognostic models Objective To evaluate the SAPS 3 score  predictive ability of hospital mortality in a large external validation cohort . Design Prospect i ve observational study . Setting and patients A total of 28,357 patients from 147 Italian ICUs joining the Project Margherita national data base of the Gruppo italiano per la Valutazione degli interventi in Terapia Intensiva ( GiViTI ) . Interventions None . MeasurementEvaluation of discrimination through ROC analysis and of overall goodness-of-fit through the Cox calibration test . Main results Although discrimination was good , calibration turned out to be poor . The general and the South-Europe Mediterranean countries equations overestimated hospital mortality overall ( SMR values 0.73 with 95 % CI 0.72–0.75 for both equations ) and homogeneously across risk classes . Overprediction was confirmed among important subgroups , with SMR values ranging between 0.47 and 0.82 . Con 
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Conclusions Statistically significant departure from perfect calibration for the SAPS 3 general equation was common in validation studies and was correlated with larger studies , as should be expected , since H-L statistics ( both C and H ) are strongly dependent on sample size This finding was also present when major geographic customized equations were evaluated .
Local customizations , on the other h and , improved SAPS 3 calibration .
Discrimination was almost always very good or excellent , which gives excellent perspectives for local customization when a precise local estimate is needed