***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 The consumption of beverages that contain sugar is associated with overweight , possibly because liquid sugars do not lead to a sense of satiety , so the consumption of other foods is not reduced . However , data are lacking to show that the replacement of sugar-containing beverages with noncaloric beverages diminishes weight gain . METHODS We conducted an 18-month trial involving 641 primarily normal-weight children from 4 years 10 months to 11 years 11 months of age . Participants were r and omly assigned to receive 250 ml ( 8 oz ) per day of a sugar-free , artificially sweetened beverage ( sugar-free group ) or a similar sugar-containing beverage that provided 104 kcal ( sugar group ) . Beverages were distributed through schools . At 18 months , 26 % of the children had stopped consuming the beverages ; the data from children who did not complete the study were imputed . RESULTS The z score for the body-mass index ( BMI , the weight in kilograms divided by the square of the height in meters ) increased on average by 0.02 SD units in the sugar-free group and by 0.15 SD units in the sugar group ; the 95 % confidence interval ( CI ) of the difference was -0.21 to -0.05 . Weight increased by 6.35 kg in the sugar-free group as compared with 7.37 kg in the sugar group ( 95 % CI for the difference , -1.54 to -0.48 ) . The skinfold-thickness measurements , waist-to-height ratio , and fat mass also increased significantly less in the sugar-free group . Adverse events were minor . When we combined measurements at 18 months in 136 children who had discontinued the study with those in 477 children who completed the study , the BMI z score increased by 0.06 SD units in the sugar-free group and by 0.12 SD units in the sugar group ( P=0.06 ) . CONCLUSIONS Masked replacement of sugar-containing beverages with noncaloric beverages reduced weight gain and fat accumulation in normal-weight children . ( Funded by the Netherl and s Organization for Health Research and Development and others ; DRINK Clinical Trials.gov number , NCT00893529 . ) OBJECTIVE To study the relationship between  men 's meat intake  and clinical outcomes in couples undergoing infertility treatment with the use of assisted reproductive technology ( ART ) . DESIGN Prospect i ve cohort study . SETTING Fertility center . PATIENT(S ) A total of 141 men whose female partners underwent 246 ART cycles from 2007 to 2014 . INTERVENTION(S ) None . Total and specific types of meat intake were estimated from dietary question naires . MAIN OUTCOME MEASURE(S ) Fertilization , implantation , clinical pregnancy , and live-birth rates per initiated cycle . Mixed-effects models account for multiple IVF cycles per woman . RESULT ( S ) There was a positive association between poultry intake and fertilization rate , with a 13 % higher fertilization rate among men in the highest quartile of poultry intake compared with those in the lowest quartile ( 78 % vs. 65 % ) . Processed meat intake was inversely related to fertilization rate in conventional IVF cycles but not in IVF cycles using intracytoplasmic sperm injection . The adjusted fertilization rates for men in increasing quartiles of processed meat intake were 82 % , 67 % , 70 % , and 54 % in conventional IVF cycles . Men 's total meat intake , including intake of specific types of meat , was not associated with implantation , clinical pregnancy , or live-birth rates . CONCLUSION ( S ) Poultry intake was positively associated with fertilization rates , whereas processed meat intake was negatively associated with fertilization rates among couples undergoing conventional IVF . This , however , did not translate into associations with clinical pregnancy or live-birth rates Animal studies and human intervention trials have demonstrated the cancer chemopreventive properties of plant phytoestrogens , and phytoestrogen supplements are now widely available ' over-the-counter ' . However , consumption of phytoestrogen-rich diets can cause impaired fertility and reproductive tract disorders in some animals and the apparent decline in human sperm quality over recent decades may be related to increased exposure to environmental endocrine disruptors . The present study determines the effects of a short-term phytoestrogen supplement on semen quality and serum sex steroid and gonadotrophin levels in human males . Healthy volunteers took a supplement containing 40 mg of isoflavones daily for 2 months and donated blood and semen sample s monthly for 2 months before and 4 months after supplementation . Semen sample s were analysed for ejaculate volume , sperm concentration , total sperm count , motility and morphology . Blood sample s were analysed for sex hormone and gonadotrophin levels and phytoestrogen concentrations , and testicular volume was measured using an orchidometer . The phytoestrogen supplement increased plasma genistein and daidzein concentrations to approx . 1 microM and 0.5 microM respectively ; yet , there was no observable effect on endocrine measurements , testicular volume or semen parameters over the study period . This is the first study to examine the effects of a 
    Output:
    
Generally , the results indicated that healthy diets rich in some nutrients such as omega-3 fatty acids , some antioxidants ( vitamin E , vitamin C , β-carotene , selenium , zinc , cryptoxanthin and lycopene ) , other vitamins ( vitamin D and folate ) and low in saturated fatty acids and trans-fatty acids were inversely associated with low semen quality parameters .
Fish , shellfish and seafood , poultry , cereals , vegetables and fruits , low-fat dairy and skimmed milk were positively associated with several sperm quality parameters .
However , diets rich in processed meat , soy foods , potatoes , full-fat dairy and total dairy products , cheese , coffee , alcohol , sugar-sweetened beverages and sweets have been detrimentally associated with the quality of semen in some studies .
As far as fecundability is concerned , a high intake of alcohol , caffeine and red meat and processed meat by males has a negative influence on the chance of pregnancy or fertilization rates in their partners .
WIDER IMPLICATION S Male adherence to a healthy diet could improve semen quality and fecundability rates .