***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 There is now convincing evidence that when an injured patient receives compensation , their long-term outcomes are worse . The effect of compensation is compounded when there is contact with the legal profession , especially if there is protracted litigation . Compensation appears to have a deleterious association with many forms of surgery , with a recent systematic review reporting that only one study out of 211 showed a possible positive effect . It is unacceptable that such an important influence on outcome from injury should continue without further investigation or action . Over the years there have been many reports of compensationrelated illness , mostly related to relatively minor and ill-defined conditions , such as chronic back pain and repetitive strain injury . More recently , there has been concern that the influence of compensation is associated with poor recovery from significant , objective ly characterised injuries such as long bone fractures and head injuries . These injuries would be expected to result in a long convalescence but there is evidence that the length of recovery is greater in the compensated group . This is important because these cl aims of the existence of injury can not be dismissed as fabrication , misdiagnosis by health professionals or malingering . The initial physical injury and treatment are beyond dispute . It is unlikely that the injured person planned or schemed to suffer the initial insult . Despite this , patients have taken longer to recover , have had lower return to work rates and poorer mental health outcomes . The question of whether this is just an association or whether there is a causal relationship is difficult to answer definitively as there is unlikely to ever be a r and omised controlled trial . There are however precedents for accepting that an association is causal when there is such a strong and consistent association . The best public health example is the association of smoking and lung cancer . There has never been a controlled trial , and it would be unethical to conduct one , however few medical practitioners would doubt the influence of smoking on lung cancer , despite the evidence being only that of association . The statistician who initially reported the association , and promoted the causal relationship ( Bradford Hill ) , was responsible for describing a set of criteria that were useful in ascribing causality to an association . Hill ’s criteria for causality of association were : ( i ) temporality ; ( ii ) strength of association ; ( iii ) biological gradient ( dose – response ) ; ( iv ) consistency ; ( v ) specificity ; ( vi ) plausibility ; ( vii ) coherence ; ( viii ) experiment ; and ( ix ) analogy . There is a strong , consistent , temporal relationship between compensation and delayed recovery from injury . There are also plausible reasons for a causal relationship . It is intuitive that protracted involvement with courts and the consequent focus on pain and suffering , could influence recovery . Recent evidence would suggest that there is some direct link with litigation itself , but this does not explain all of the compensation effects . There Background : Exposure to a whiplash injury implies a risk for development of chronic disability and h and icap , with reported frequencies ranging from 0 % to 50 % in follow-up studies . The exact risk for development of chronic whiplash syndrome is not known . Objective : To prospect ively determine the sensitivity and specificity of five possible predictors for h and icap following a whiplash injury . Methods : In a 1-year prospect i ve study of persons with acute whiplash injury ( n = 141 ) and control subjects who had acute ankle distortion ( n = 40 ) , pain intensity , number of nonpainful neurologic complaints , cervical mobility , workload during extension and flexion of the neck , and results of psychometric assessment were recorded . The consecutively sample d injured persons were assessed with structured and semistructured question naires , and underwent neurologic examination after 1 week and 1 , 3 , 6 , and 12 months . After 3 to 4 years , participants with whiplash injury were question ed about legal issues . Results : After 1 year , 11 ( 7.8 % ) persons with whiplash injury had not returned to usual level of activity or work . The best single estimator of h and icap was the cervical range-of-motion test , which had a sensitivity of 73 % and a specificity of 91 % ( p < 0.01 , Cox regression analysis ) . Accuracy and specificity increased to 94 % and 99 % when combined with pain intensity and other complaints . This increase was gained at the expense of a reduced sensitivity . Initiation of lawsuit within first month after injury did not influence recovery . Conclusion : The cervical range-of-motion test has a high sensitivity in prediction of h and icap after acute whiplash injury . The value of cervical range-of-motion test is further improved by additional recording of symptoms and pain intensity Abstract Higher initial levels of pain and disability , older age , cold hyperalgesia , impaired sympathetic vasoconstriction and moderate post‐traumatic stress symptoms have been shown to be associated with poor outcome 6 months following whiplash injury . This study prospect ively investigated the predictive capacity of these variables at a long‐term follow‐up . Sixty‐five of an initial cohort of 76 acutely injured whiplash participants were followed to 2–3 years post‐accident .  Motor function ( ROM ; kinaesthetic sense ; activity of the superficial neck flexors ( EMG ) during cranio‐cervical flexion ) , quantitative sensory testing ( pressure , thermal pain thresholds and brachial plexus provocation test ) , sympathetic vasoconstrictor responses and psychological distress ( GHQ‐28 , TSK and IES ) were measured . The outcome measure was Neck Disability Index ( NDI ) scores . Particip 
    Output:
    
Irrespective of the compensation-related factor involved and the health outcome measured , the quality of these studies was similar to studies that did not find a significant negative association : most took some measures to address selection bias , confounding , and measurement bias , and none resolved the potential for reverse causality bias that arises in the relationship between compensation-related factors and health .
Consequently , there is no clear evidence to support the idea that compensation and its related processes lead to worse health