***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: OBJECTIVE The SAD PERSONS scale is a widely used risk assessment tool for suicidal behavior despite a paucity of supporting data . The objective of this study was to examine the ability of the scale in predicting suicide attempts . METHOD Participants consisted of consecutive referrals ( N=4,019 ) over 2 years ( January 1 , 2009 to December 31 , 2010 ) to psychiatric services in the emergency departments of the 2 largest tertiary care hospitals in the province of Manitoba , Canada .  SAD PERSONS and Modified SAD PERSONS ( MSPS ) scale scores were recorded for individuals at their index and all subsequent presentations . The 2 main outcome measures in the study included current suicide attempts ( at index presentation ) and future suicide attempts ( within the next 6 months ) . The ability of the scales to predict suicide attempts was evaluated with logistic regression , sensitivity and specificity analyses , and receiver operating characteristic curves . RESULTS 566 people presented with suicide attempts ( 14.1 % of the sample ) . Both SAD PERSONS and MSPS showed poor predictive ability for future suicide attempts . Compared to low risk scores , high risk baseline scores had low sensitivity ( 19.6 % and 40.0 % , respectively ) and low positive predictive value ( 5.3 % and 7.4 % , respectively ) . SAD PERSONS did not predict suicide attempts better than chance ( area under the curve = 0.572 ; 95 % confidence interval [ CI ] , 0.51 - 0.64 ; P value nonsignificant ) . Stepwise regression identified 5 original scale items that accounted for the greatest proportion of future suicide attempt variance . High risk scores using this model had high sensitivity ( 93.5 % ) and were associated with a 5-fold higher likelihood of future suicide attempt presentation ( odds ratio = 5.58 ; 95 % CI , 2.24 - 13.86 ; P<.001 ) . CONCLUSION In their current form , SAD PERSONS and MSPS do not accurately predict future suicide attempts 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 Rates of self-harm are high and have recently increased . This trend and the repetitive nature of self-harm pose a significant challenge to mental health services . AIMS To determine the efficacy of a structured group problem-solving skills training ( PST ) programme as an intervention approach for self-harm in addition to treatment as usual ( TAU ) as offered by mental health services . METHOD A total of 433 participants ( aged 18 - 64 years ) were r and omly assigned to TAU plus PST or TAU alone . Assessment s were carried out at baseline and at 6-week and 6-month follow-up and repeated hospital-treated self-harm was ascertained at 12-month follow-up . RESULTS The treatment groups did not differ in rates of repeated self-harm at 6-week , 6-month and 12-month follow-up . Both treatment groups showed significant improvements in psychological and social functioning at follow-up . Only one measure ( needing and receiving practical help from those closest to them ) showed a positive treatment effect at 6-week ( P = 0.004 ) and 6-month ( P = 0.01 ) follow-up . Repetition was not associated with waiting time in the PST group . CONCLUSIONS This brief intervention for self-harm is no more effective than treatment as usual . Further work is required to establish whether a modified , more intensive programme delivered sooner after the index episode would be effective The 2013 US Veterans Administration/Department of Defense Clinical Practice Guidelines ( VA/DoD CPG ) require comprehensive suicide risk assessment s for VA/DoD patients with mental disorders but provide minimal guidance on how to carry out these assessment s. Given that clinician-based assessment s are not known to be strong predictors of suicide , we investigated whether a precision medicine model using administrative data after outpatient mental health specialty visits could be developed to predict suicides among out patients . We focused on male nondeployed Regular US Army soldiers because they account for the vast majority of such suicides . Four machine learning classifiers ( naive Bayes , r and om forests , support vector regression and elastic net penalized regression ) were explored . Of the Army suicides in 2004–2009 , 41.5 % occurred among 12.0 % of soldiers seen as outpatient by mental health specialists , with risk especially high within 26 weeks of visits . An elastic net classifier with 10–14 predictors optimized sensitivity ( 45.6 % of suicide deaths occurring after the 15 % of visits with highest predicted risk ) . Good model stability was found for a model using 2004–2007 data to predict 2008–2009 suicides , although stability decreased in a model using 2008–2009 data to predict 2010–2012 suicides . The 5 % of visits with highest risk included only 0.1 % of soldiers ( 1047.1 suicides/100 000 person-years in the 5 weeks after the visit ) . This is a high enough concentration of risk to have implication s for targeting preventive interventions . An even better model might be developed in the future by including the enriched information on clinician-evaluated suicide risk m and ated by the VA/DoD CPG to be recorded OBJECTIVE The authors evaluated the effectiveness of brief cognitive-be 
    Output:
    
Risk assessment methods have been shown to be sensitive predictors of suicide and suicide attempts , but the frequency of false positives limits their clinical utility .