***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 Despite major advances in the underst and ing of the neurobiologic mechanisms of pain , the wide variation in acute pain experience has not been well explained . Therefore , the authors investigated the potential of a preoperatively induced heat injury to predict subsequent postoperative pain ratings in patients undergoing knee surgery . Methods Twenty patients were studied . The burn injury was induced 6 days before surgery with a contact thermode ( 12.5 cm2 , 47 ° C for 7 min ) . The sensory testing , before and 1 h after the injury , included pain score during induction of the burn , secondary hyperalgesia area , thermal and mechanical pain perception , and pain thresholds . Postoperative analgesia consisted of ibuprofen and acetaminophen . Pain ratings ( visual analog scale ) at rest and during limb movement were followed for 10 days after surgery . Results The burn injury was associated with development of significant hyperalgesia . There was a significant correlation between preoperative pain ratings during the burn injury and early ( 0–2 days , area under the curve ) and late ( 3–10 days , area under the curve ) postoperative dynamic pain ratings during limb movement . Conclusion The results of this study suggest that the pain response to a preoperative heat injury may be useful in research in predicting the intensity of postoperative pain . These findings may have important implication s to identify patients at risk for development of chronic pain and to stratify individuals for investigations of new analgesics Abstract Opioids can elicit unexpected changes in pain sensitivity , known as opioid‐induced hyperalgesia ( OIH ) . The aim of this study was to explore whether OIH exists in patients with chronic pain treated with oral opioids ( OP ) versus non‐opioid ( NOP ) analgesics .  The sensitivity to cold pain and the magnitude of diffuse noxious inhibitory control ( DNIC ) were evaluated in 73 OP and 37 NOP treated patients . Pain threshold , intensity and tolerance in response to the cold pressor ( 1 ° C ) were measured . DNIC was tested by co‐administrating conditioned heat stimulation ( 47 ° C ) to the left forearm and a conditioning stimulation of 12 ° C for 30 s to the right h and . The results showed no differences between the two groups in any of the cold pain measures . In contrast , the magnitude of DNIC was significantly larger in the NOP than in the OP treated patients ( p = 0.003 ) . A gender based analysis showed a significant difference in DNIC between OP and NOP treated men only . However , a mixed model ANOVA demonstrated a significant effect of treatment ( OP versus NOP ) ( F = 5.928 , p = 0.017 ) rather than gender on DNIC . A regression analysis showed that opioid dosage and treatment duration had a significant negative effect on the magnitude of DNIC in OP treated men ( β = −2.175 , p = 0.036 and β = −2.061 , p = 0.047 , respectively ) . In conclusion , oral opioids usage for the treatment of chronic pain does not result in abnormal sensitivity to cold pain , but seems to alter pain modulation . The use of ‘ advanced ’ psychophysics tests such as evaluation of DNIC can help underst and ing the phenomenon of OIH BACKGROUND : Recently , a prediction rule was developed to preoperatively predict the risk of severe pain in the first postoperative hour in surgical in patients . We aim ed to modify the rule to enhance its use in both surgical in patients and out patients ( ambulatory patients ) . Subsequently , we prospect ively tested the modified rule in patients who underwent surgery later in time and in another hospital ( external validation ) . METHODS : The rule was originally developed from the data of 1395 adult in patients . We modified the rule with the data of 549 out patients who underwent surgery between 1997 and 1999 in the same center ( Academic Medical Center Amsterdam , The Netherl and s ) . Furthermore , we tested the performance of the modified rule in 1035 in- and out patients who underwent surgery in 2004 , in the University Medical Center Utrecht , The Netherl and s ( external validation ) . Performance was quantified by the rule 's calibration ( agreement between observed frequencies and predicted risks ) and discrimination ( ability to distinguish between patients at high and low risk ) . RESULTS : Modification of the original rule to enhance prediction in out patients included reclassification of the predictor “ type of surgery , ” addition of the predictor “ surgical setting ” ( ambulatory surgery : yes/no ) and addition of interaction terms between surgical setting and the other predictors . One-third of the patients in the Utrecht cohort reported severe postoperative pain ( 36 % ) , compared to 62 % of the patients in the Amsterdam cohort . The distribution of most predictors was similar in the two cohorts , although the patients in the Utrecht cohort were slightly older , more often underwent ambulatory surgery and had large expected incision sizes less often than patients in the Amsterdam cohort . The modified prediction rule showed good calibration , when an adjusted intercept was used for the lower incidence in the Utrecht cohort . The discrimination was reasonable ( area under the Receiver Operating Characteristic curve 0.65 [ 95 % confidence interval 0.57–0.73 ] ) . CONCLUSIONS : A previously developed prediction rule to predict severe postoperative pain was modified to allow use in both in patients and out patients . By validating the rule in patients who underwent surgery several years later in another hospital , it was shown that the rule could be generalized in time and place 
    Output:
    
The predictive strength is much higher than previously reported for single factor analyses of demographics and psychologic factors .
In addition , some of these studies indicate that an increase in preoperative pain sensitivity is associated with a high probability of development of sustained postsurgical pain