***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 OBJECTIVES To compare black and white nursing home residents with respect to the incidence of nursing home (NH)-acquired pressure ulcers ( PUs )  and to examine the role of resident characteristics and facility characteristics in explaining differences between the racial groups . DESIGN Prospect i ve cohort study conducted between 1992 and 1995 . SETTING Fifty-nine Maryl and NHs . PARTICIPANTS A total of 1,938 residents ( 301 black , 1,637 white ) aged 65 and older newly admitted to participating NHs . MEASUREMENTS The outcome variable was the first occurrence of a Stage 2 , 3 , or 4 PU as determined based on medical record review . The predictor variable was race ( black , white ) . Eight resident characteristics ( age , sex , number of activity of daily living dependencies , bedfast , PU on admission to facility , incontinence , dementia , and whether the resident was on Medicaid ) and three facility characteristics ( number of beds , for-profit ownership status , and urban/nonurban location ) were considered as possible confounding variables . RESULTS The incidence of PUs was 0.38 per person-year in the NH . The rate for blacks was significantly higher than for whites ( 0.56 vs 0.35 per person-year ) ( P<.001 ) . In multivariate analysis , controlling for eight resident characteristics and three facility characteristics , race was significantly associated with PU incidence ( hazard ratio comparing blacks with whites=1.31 , 95 % confidence interval=1.02 - 1.66 ) . CONCLUSION Blacks have a higher incidence of NH-acquired PUs than whites ; resident characteristics appear to mediate the higher risk . Future research should aim to identify modifiable factors that explain differences between racial groups in PU risk and to develop solutions to prevent the suffering and cost associated with PUs Patients undergoing surgery are prone to develop pressure ulcers during surgery . The aim of this study was to identify  pressure ulcer risk indicators in patients undergoing surgery which lasted more than four hours . A prospect i ve follow-up study was conducted in 208 patients . Data on presence or absence of pressure ulcers and risk indicators were collected preoperatively , during surgery , and postoperatively . The results show that of the many indicators recorded , the only predictor of pressure ulcers was length of surgery . Because it is not possible to influence the length of the surgery , prevention should primarily be aim ed at decreasing pressure and shearing forces during surgery The purpose of this prospect i ve study was to determine the incidence of pressure ulcers and to examine factors related to pressure ulcer development in patients in an acute care setting . Adult medical and surgical patients who were free of pressure ulcers at admission were assessed within 36 hours of admission and then three times per week for 2 weeks or until discharge . Instruments included a  demographic data form , a skin assessment form , and the Braden Scale for Predicting Pressure Sore Risk .  Most subjects had 46 assessment s completed . The sample consisted of 149 subjects , with a pressure ulcer incidence rate of 13.4 % ( n = 20 ) . Subjects who acquired pressure ulcers had lower hemoglobin levels ( t = 2.17 , p = 0.03 ) , spent more time in bed ( t = 3.90 , p = 0.0001 ) , and spent less time in a chair ( t = 3.2 , p = 0.002 ) than those who did not acquire pressure ulcers . A stepwise logistic regression analysis was used to calculate risk of pressure ulcer development . In the final model , hemoglobin level and hours spent in bed continued to be predictors of pressure ulcer development ( chi 2 = 9.306 , df = 2 , p = 0.0095 ) . All 20 subjects who acquired pressure ulcers were further categorized into groups with stage I ( n = 12 ) or stage II ( n = 8) ulcers . Patients with stage I pressure ulcers were primarily receiving post-surgical care ( 67 % ) , whereas patients who acquired stage II ulcers had medical conditions that affected tissue perfusion , such as respiratory diseases ( 50 % ) and diabetes mellitus ( 12 % ) DESIGN Nonexperimental-Descriptive . SAMPLE The inclusion criteria were trauma patients , ages 15 and older , who were hospitalized for > 2 days , and who did not have preexisting skin breakdown . A total of 148 consecutive trauma patients admitted to the study institution meeting the inclusion criteria were prospect ively enrolled . METHODS Patients were assessed every 3 days for skin breakdown . Information on the patient 's bed type , therapies , medical devices , and nutrition was collected . The Braden Scale for predicting pressure ulcer risk was completed at each assessment . FINDINGS Of the 148 patients enrolled , 30 developed at least one area of skin breakdown for a prevalence of 20.3 % in patients hospitalized more than 2 days . The most common cause of breakdown was positional pressure ( 47.4 % ) . Cervical collars were the second leading cause at 23.7 % , followed by tracheostomy/endotracheal tubes at 10.5 % . The mobility subscale of the Braden Pressure Ulcer Risk Assessment tool was significantly predictive of skin breakdown ( p < .001 ) . IMPLICATION S FOR NURSING RESEARCH Skin breakdown is a significant problem in trauma patients who are hospitalized for more 
    Output:
    
Risk factors emerging most frequently as independent predictors of pressure ulcer development included three primary domains of mobility/activity , perfusion ( including diabetes ) and skin/pressure ulcer status .
Skin moisture , age , haematological measures , nutrition and general health status are also important , but did not emerge as frequently as the three main domains .
There is limited evidence that either race or gender is important .
Overall there is no single factor which can explain pressure ulcer risk , rather a complex interplay of factors which increase the probability of pressure ulcer development .