***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: Background : Patients who develop castration-resistant prostate cancer ( CRPCa ) typically continue on and rogen deprivation therapy ( ADT ) . Whether these patients need to remain on ADT has not been well studied . We conducted a multicenter r and omized trial to compare an intermittent versus continuous approach to ADT in CRPCa patients .  Overall survival , health-related quality of life ( QOL ) , and cost were the main endpoints . Methods : CRPCa patients were r and omized 2:1 to intermittent or continuous luteinizing hormone-releasing hormone agonists ( LHRHa ) .  Patients were followed with clinical assessment s , laboratory investigations , and QOL question naires ( EORTC QLQ-C30 or PROSQOLI ) every 2 months . If the serum testosterone rose above castrate levels ( 1.75 nmol/L ) , LHRHa were reinitiated . The study was design ed to close if > 50 % of patients needed to restart ADT in the intermittent arm . Results : Thirty-one patients were followed with a median follow-up of 26.8 months—18 in the intermittent arm and 13 in the continuous . Twelve of 18 patients on the intermittent arm were reinitiated on LHRHa at a median time of 17.9 months . There was no difference in overall or cancer-specific survival between the 2 arms . There was no statistically significant difference in QOL between the 2 arms at 0 and 12 months . The total mean costs at 24 months were significantly lower in the intermittent arm ( $ 3135 vs. $ 8253 Canadian dollars , P=0.0167 ) compared with the continuous . The main limitation of this study is the small sample size . Conclusions : We have observed that intermittent ADT in patients with CRPCa , using a testosterone of > 1.75 ngmol/L as a trigger to reinitiate LHRHa , results in a substantial cost savings with no negative impact on oncologic and QOL outcomes As professionals , we want to use the best treatments ; as patients , we want to be given them . Knowing whether an intervention works ( or does not work ) is fundamental to clinical decision making . However , clinical decision making involves more than simply taking published results of research directly to the bedside . Physicians need to consider how similar their patients are to those in the published studies , to take the values and preferences of their patients into account , and to consider their own experience with a given test or treatment . Evidence from clinical research is becoming increasingly important in medical- practice decisions as more and better evidence is published . But when is the evidence strong enough to justify changing a practice ? Individual studies that involve only small numbers of patients may have results that are distorted by the r and om play of chance and thus lead to less than optimal decisions . As is clear from other papers in this series , systematic review s identify , critically appraise , and review all the relevant studies on a clinical question and are more likely to give a valid answer . They use explicit methods and quality st and ards to reduce bias . Their results are the closest we can come to reaching the truth given our current state of knowledge . The questions about an intervention that a systematic review should answer are the following : 1 . Does it work ? 2 . If it works , how well does it work in general and compared with placebo , no treatment , or other interventions that are currently in use ? 3 . Is it safe ? 4 . Will it be safe and effective for my patients ? Whereas the critical appraisal and qualitative synthesis provided by review articles can be interpreted directly , the numerical products of quantitative review s can be more difficult to underst and and apply in daily clinical practice . This paper provides guidance on how to interpret the numerical and statistical results of systematic review s , translate these results into more underst and able terms , and apply them directly to individual patients . Many of these principles can also be used to interpret the numerical results of individual clinical studies . They are particularly relevant to systematic review s , however , because such review s contain more information than do primary studies and often exert greater influence than do individual studies . Making Sense of the Numerical Results of Clinical Studies Although the results of clinical studies can be expressed in intuitively meaningful ways , such results do not always easily translate into clinical decision making . For example , results are frequently expressed in terms of risk , which is an expression of the frequency of a given outcome . ( Risks are probabilities , which can vary between 0.0 and 1.0 . A probability of 0.0 means that the event will never happen , and a probability of 1.0 means that it always happens . ) Consider a hypothetical study of the recurrence of migraine headaches in a control group receiving placebo and a treatment group receiving a new antimigraine preparation , drug M ( a secondary prevention trial ) . Suppose that at the end of the trial , migraines recurred in 30 % of the control group ( the risk for recurrence was 0.30 ) but in only 5 % of the drug M group ( risk of 0.05 ) ( Table 1 ) . Table 1 . Numerical Expression of Hypothetical Clinical Trial Results The outcomes of the study are clear enough for the two groups when they are examined separately . But clinicians and patients are more interested in the comparative results , that is , the outcome in one group relative to the outcome in the other group . This overall ( comparative ) result can be expressed in various ways . For example , the relative risk , which is the risk in the treatment group relative to that in the control group , is simply the ratio of the risks in the two groups . In other words , relative risk is the risk in the treatment group divided by that in the control group , 0.05 0.30 , or 0.17 . The comparison can also be expressed as the reduction in relative risk , which is the ratio between the decrease in risk ( in the treatment group ) and the risk in the control group , 0.25 0.30 , or 0.83 ( Table 1 ) . ( The relative risk reduction can also be calculated as 1 relative risk ) . Although the clinical meaning of relative risk ( and relative risk reduction ) is reasonably clear , relative risk has the distinct disadvantage that a given value ( for example , 0.17 ) is the same whether the risk with treatment decreases from 0.80 to 0.14 , from 0.30 to 0.05 , from 0.001 to 0.00017 , and so forth . The clinical implication s of these changes clearly differ from one another enormously and depend on the specific disease and intervention . An important alternate expression of comparative results , therefore , is the absolute risk reduction . Absolute risk reduction is determined by subtracting the risk in one group from the risk in the other ( for example , the risk in the treatment group is subtracted from the risk in the placebo group ) . In the case of our migraine study , the absolute risk reduction would be 0.30 0.05 , which equals 0.25 , or 25 percentage points . In contrast , for a study in which the risk decreased from 0.001 to 0.00017 , the absolute risk reduction would be only 0.00083 , or 0.083 percentage points , which is a trivial change in comparison ( Table 1 ) . This arithmetic emphasizes the difficulty of expressing the results of clinical studies in meaningful ways . Relative risk and relative risk reduction clearly give a quantitative sense of the effects of an intervention in proportional terms but provide no clue about the size of an effect on an absolute scale . In contrast , although it tells less about proportional effects , absolute risk says a great deal about whether an effect is likely to be clinical ly meaningful . Despite this benefit , even absolute risk is problematic because it is a dimensionless , abstract number ; that is , it lacks a direct connection with the clinical situation in which the patient and physician exist . However , another way of expressing clinical research results can provide that clinical link : the number needed to treat ( NNT ) . Number Needed To Treat The NNT for a given therapy is simply the reciprocal of the absolute risk reduction for that treatment [ 1 , 2 ] . In the case of our hypothetical migraine study ( in which risk decreased from 0.30 without treatment with drug M to 0.05 with treatment with drug M , for a relative risk of 0.17 , a relative risk reduction of 0.83 , and an absolute risk reduction of 0.25 ) , the NNT would be 1 0.25 , or 4 . In concrete clinical terms , an NNT of 4 means that you would need to treat four patients with drug M to prevent migraine from recurring in one patient . To emphasize the difference between the concepts embodied in NNT and relative risk , recall the various situations mentioned above , in all of which the relative risk was 0.17 but in which the absolute risk decreased from 0.80 to 0.14 in one case and from 0.001 to 0.00017 in another . Note that the corresponding NNTs in these two other cases are 1.5 and 1204 , respectively : that is , you would need to treat 1.5 and 1204 patients to obtain a therapeutic result in these two situations compared with 4 patients with drug M ( Table 1 ) . The NNT can be calculated easily and kept as a single numerical reminder of the effectiveness ( or , as we will see , the potential for harm ) of a particular therapy . As we suggested , the NNT has the crucial advantage of direct applicability to clinical practice because it shows the effort that is required to achieve a particular therapeutic target . The NNT has the additional advantage that it can be applied to any beneficial outcome or any adverse event ( when it becomes the number needed to harm [ NNH ] ) . The concept of NNT always refers to a comparison group ( in which patients receive placebo , no treatment , or some other treatment ) , a particular treatment outcome , and a defined period of treatment . In other words , the NNT is the number of patients that you will need to treat with drug or treatment A to achieve an improvement in outcome compared with drug or treatment B for a treatment period of C weeks ( or other unit of time ) . To be fully specified , NNT and NNH must always specify the comparator , the therapeutic outcome , and the duration of treatment that is necessary to achieve the outcome . Important Qualities of the Number Needed To Treat The NNT is treatment specific . It describes the difference between treatment and control in achieving a particular clinical outcome . Table 2 shows NNTs from a selection of systematic review s and large r and omized , controlled trials . Table 2 . Numbers Needed To Treat from Systematic Review s and R and omized , Controlled Trials A very small NNT ( that is , one that approaches 1 ) means that a favorable outcome occurs in nearly every patient who receives the treatment and in few patients in a comparison group . Although NNTs close to 1 are theoretically possible , they are almost never found in practice . However , small NNTs do occur in some therapeutic trials , such as those comparing antibiotics with placebo in the eradication of Helicobacter pylori infection or those examining the use of insecticide for head lice ( Table 2 ) . An NNT of 2 or 3 indicates that a treatment is quite effective . In contrast , such prophylactic interventions as adding aspirin to streptokinase to reduce 5-week vascular mortality rates after myocardial infa rct ion may have NNTs as high as 20 to 40 and still be considered clinical ly effective . Limitations of the Number Needed To Treat Although NNTs are powerful instruments for interpreting clinical effects , they also have important limitations . First , an NNT is generally expressed as a single number , which is known as its point estimate . As with all experimental measurements , however , the true value of the NNT can be higher or lower than the point estimate determined through clinical studies . The 95 % CIs of the NNT are useful in this regard because they provide an indication that BACKGROUND Castration resistance occurs in most patients with metastatic hormone-sensitive prostate cancer who are receiving and rogen-deprivation therapy .  Replacing and rogens before progression of the disease is hypothesized to prolong and rogen dependence . METHODS Men with newly diagnosed , metastatic , hormone-sensitive prostate cancer , a performance status of 0 to 2 , and a prostate-specific antigen ( PSA ) level of 5 ng per milliliter or higher received a luteinizing hormone-releasing hormone analogue and an anti and rogen agent for 7 months . We then r and omly assigned patients in whom the PSA level fell to 4 ng per milliliter or lower to continuous or intermittent and rogen deprivation  , with patients stratified according to prior or no prior hormonal therapy , performance status , and extent of disease ( minimal or extensive ) . The co primary objectives were to assess whether intermittent  therapy  was noninferior to continuous therapy with respect to survival , with a one-sided test with an upper boundary of the hazard ratio of 1.20 , and whether quality of life differed between the groups 3 months after r and omization . RESULTS A total of 3040 patients were enrolled , of whom 1535 were included in the analysis : 765 r and omly assigned to continuous and rogen deprivation and 770 assigned to intermittent and rogen deprivation . The median follow-up period was 9.8 years . Median survival was 5.8 years in the continuous-therapy group and 5.1 years in the intermittent-therapy group ( hazard ratio for death with intermittent therapy , 1.10 ; 90 % confidence interval , 0.99 to 1.23 ) . Intermittent therapy was associated with better erectile function and mental health ( P<0.001 and P=0.003 , respectively ) at month 3 but not thereafter . There were no significant differences between the groups in the number of treatment-related high- grade adverse events . CONCLUSIONS Our findings were statistically inconclusive . In patients with metastatic hormone-sensitive prostate cancer , the confidence interval for survival exceeded the upper boundary for noninferiority , suggesting that we can not rule out a 20 % greater risk of death with intermittent therapy than with continuous therapy , but too few events occurred to rule out significant inferiority of intermittent therapy . Intermittent therapy result ed in small improvements in quality of life . ( Funded by the National Cancer Institute and others ; Clinical Trials.gov number , NCT00002651 . 
    Output:
    
Conclusion Overall survival was similar between IAD and CAD in patients with locally advanced , recurrent or metastatic hormone-sensitive prostate cancer .
Data on CSS are weak and the benefits of IAD on this outcome remain uncertain .
Impact in QoL was similar for both groups , however , sexual activity scores were higher and the incidence of hot flushes was lower in patients treated with IAD