***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: & NA ; The large inter‐individual variability in the magnitude of analgesia in response to opioids and the high prevalence of adverse events associated with their use underline the clinical importance of being able to predict who will or will not respond to opioid treatment . The present study used both static and dynamic quantitative sensory testing ( QST ) on 40 healthy volunteers in order to test whether this methodology can predict the analgesic effects of oral oxycodone , as compared to a placebo , on latency to onset , pain intensity , and tolerance to the cold pressor test ( CPT ) . Static QST consisted of measuring heat and cold pain thresholds . Dynamic QST included measurements of the magnitude of the diffuse noxious inhibitory control (DNIC)‐like effect and of temporal summation ( TS ) . Results showed that oxycodone , but not the placebo , significantly elevated the latency and tolerance to cold pain and significantly reduced pain intensity . The static QST results showed that heat pain thresholds predicted the magnitude of reduction in pain intensity in response to oxycodone treatment ( F(1,22 ) = 5.63 , p = 0.027 , R2 = 0.17 ) . The dynamic QST results showed that TS predicted the effect of oxycodone on the tolerance to CPT ( F(1,38 ) = 9.11 , p = 0.005 , R2 = 0.17 ) . These results suggest that both static and dynamic QST have the potential to be useful in the prediction of the response to opioid treatment 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 To examine the prevalence of outcome reporting bias — the selection for publication of a subset of the original recorded outcome variables on the basis of the results — and its impact on Cochrane review s. Design A nine point classification system for missing outcome data in r and omised trials was developed and applied to the trials assessed in a large , unselected cohort of Cochrane systematic review s. Research ers who conducted the trials were contacted and the reason sought for the non-reporting of data . A sensitivity analysis was undertaken to assess the impact of outcome reporting bias on review s that included a single meta- analysis of the review primary outcome . Results More than half ( 157/283 ( 55 % ) ) the review s did not include full data for the review primary outcome of interest from all eligible trials . The median amount of review outcome data missing for any reason was 10 % , whereas 50 % or more of the potential data were missing in 70 ( 25 % ) review s. It was clear from the publications for 155 ( 6 % ) of the 2486 assessable trials that the research ers had measured and analysed the review primary outcome but did not report or only partially reported the results . For reports that did not mention the review primary outcome , our classification regarding the presence of outcome reporting bias was shown to have a sensitivity of 88 % ( 95 % CI 65 % to 100 % ) and specificity of 80 % ( 95 % CI 69 % to 90 % ) on the basis of responses from 62 trialists . A third of Cochrane review s ( 96/283 ( 34 % ) ) contained at least one trial with high suspicion of outcome reporting bias for the review primary outcome . In a sensitivity analysis undertaken for 81 review s with a single meta- analysis of the primary outcome of interest , the treatment effect estimate was reduced by 20 % or more in 19 ( 23 % ) . Of the 42 meta-analyses with a statistically significant result only , eight ( 19 % ) became non-significant after adjustment for outcome reporting bias and 11 ( 26 % ) would have overestimated the treatment effect by 20 % or more . Conclusions Outcome reporting bias is an under-recognised problem that affects the conclusions in a substantial proportion of Cochrane review s. Individuals conducting systematic review s need to address explicitly the issue of missing outcome data for their review to be considered a reliable source of evidence . Extra care is required during data extraction , review ers should identify when a trial reports that an outcome was measured but no results were reported or events observed , and contact with trialists should be encouraged Postoperative pain remains a significant problem and the individual variance in postoperative pain is not fully understood . In recent years , there has been focus on identifying risk factors predicting patients with high postoperative pain intensity  or consumption of analgesics , which may facilitate an improvement in rehabilitation . This study evaluates the relationship between preoperative experimental pain assessment and postoperative pain and opioid consumption .  Forty-four patients with uni- or bilateral kidney stone disease scheduled for percutaneous nephrolithotomy were included . The preoperative pain thresholds were measured using electrical ( single and 5 repeated ) and pressure pain stimulation over the flank bilaterally ( stone-side = operation side and control-side = non-operation side ) . Postoperative pain scores were recorded on a numerical rating scale and analgesic consumption was registered . The responses to repeated electrical stimuli ( temporal summation ) were preoperatively increased on the stone-side compared to the control-side ( P = 0.016 ) . Preoperative electrical pain thresholds from the control-side correlated inversely with postoperative opioid consumption ( single stimuli : ρ = −0.43 , P < 0.01 ; repeated stimuli : ρ = −0.45 , P < 0.005 ) . This correlation was more pronounced for the 22 patients with unilateral renal calculi ( single stimuli : ρ = −0.54 , P < 0.02 ; repeated stimuli : ρ = 
    Output:
    
Significant correlations were observed between responses to analgesics and several QST parameters including ( 1 ) heat pain threshold in experimental human pain , ( 2 ) electrical and heat pain thresholds , pressure pain tolerance and suprathreshold heat pain in surgical patients , and ( 3 ) electrical and heat pain threshold and conditioned pain modulation in patients with chronic pain .
Although promising , the current evidence is not sufficiently robust to recommend the use of any specific QST parameter in predicting analgesic response .