***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 : Sex-related differences in the perception of pain and susceptibility to opioids remain a matter of debate . Intravenous morphine titration used to obtain pain relief in the immediate postoperative period is a unique clinical model for assessing the effect of sex on reported pain . Because of the wide variation in dose requirements for pain management , the authors conducted a prospect i ve study in a large population and also assessed the effect of aging . Methods : Intravenous morphine titration was administered as a bolus of 2 ( body weight ≪ 60 kg ) or 3 mg ( body weight > 60 kg ) during the immediate postoperative period . The interval between each bolus was 5 min . The visual analog pain scale ( VAS ) threshold required to administer morphine was 30 , and pain relief was defined as a VAS score of 30 or less . Data are expressed as mean ± SD . Results : Data from 4,317 patients were analyzed ; 54 % of the patients were male , and 46 % were female . The mean morphine dose required to obtain pain relief was 11.9 ± 6.8 mg or 0.173 ± 0.103 mg/kg . Women had a higher initial VAS score ( 74 ± 19 vs. 71 ± 19 ; P < 0.001 ) and required a greater dose of morphine ( 0.183 ± 0.111 vs. 0.165 ± 0.095 mg/kg ; P < 0.001 ) . In contrast , no significant difference was noted in elderly ( aged > 75 yr ) patients ( 0.163 ± 0.083 vs. 0.157 ± 0.085 mg/kg ) . Conclusion : Women experienced more severe postoperative pain and required a greater dose ( + 11 % ) of morphine than men in the immediate postoperative period . This sex-related difference disappeared in elderly patients BACKGROUND : IV morphine titration ( IMT ) is widely used in the postanesthesia care unit to achieve pain relief . Numerous factors contribute to variability in postoperative pain or morphine consumption . We analyzed prospect ively the pre- and intraoperative predictive factors of severe postoperative pain defined as a dose of IMT > 0.15 mg/kg or a failure of IMT . METHODS : We assessed the role of preoperative information about pain , medical treatments , and intraoperative events and their role on postoperative pain . After IMT , patients were divided into two groups : severe pain ( SP ) and nonsevere pain . Data are expressed as mean ± sd . RESULTS : Three hundred forty-two patients were included in the study : 200 ( 58 % ) in the nonsevere pain group and 142 ( 42 % ) in the SP group . Using a univariate analysis , there was no significant difference between groups related to medical or surgical history except for more frequent preoperative treatments in the SP group ( P < 0.05 ) . Duration of the surgical procedure and anesthesia were longer in the SP group ( P < 0.001 ) . The dose of sufentanil and visual analog scale scores before and at the end of IMT were higher in the SP group ( P < 0.001 ) . Using a multivariate analysis , a high dose of  intraoperative opioid ( sufentanil dose > 0.6 μg/kg ) ( Odds ratio = 2.68 , P < 0.001 ) , a general anesthetic procedure ( Odd ratio = 3.96 , P = 0.03 ) , and the use of preoperative analgesic drugs ( Odds ratio = 1.91 , P < 0.01 ) were independent factors associated with severe postoperative pain . CONCLUSION : A higher intraoperative dose of sufentanil , general anesthesia , and preoperative treatment with analgesics were significantly associated with severe postoperative pain We have evaluated the level of state and trait anxiety , neuroticism , extroversion and coping style as predictors of the effectiveness of patient-controlled analgesia ( PCA ) in 110 patients undergoing total abdominal hysterectomy . After operation patients were allocated to receive pain control with either PCA or i.m . injections ( IMI ) .  Pain was assessed using the short form McGill pain question naire at 6 , 18 and 24 h after operation , and by recording the amount of analgesic consumed in the first 24 h after surgery . Both state anxiety and coping style were significant predictors of postoperative pain , irrespective of the method of analgesia used . Patients using PCA experienced significantly better pain control than those receiving IMI . However , it was those with high levels of state anxiety who experienced the greatest reduction in pain with PCA . In addition to achieving better pain control , patients who received PCA used significantly less analgesia and were discharged earlier than patients who received IMI Postoperative pain is a common reason for the delayed discharge and unanticipated hospital admission of out patients .In this study , we examined the pattern of  pain in ambulatory surgical patients and determined those factors that predict postoperative pain .  Ten thous and eight consecutive ambulatory surgical patients were prospect ively studied .  Preoperative patient characteristics , intraoperative variables , and pain in the postanesthesia care unit ( PACU ) and the ambulatory surgical unit ( ASU ) and 24 
    Output:
    
Gender was not found to be a consistent predictor as traditionally believed .
Early identification of the predictors in patients at risk of postoperative pain will allow more effective intervention and better management .