***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 Mitoxantrone-based chemotherapy palliates pain without extending survival in men with progressive and rogen-independent prostate cancer . We compared docetaxel plus estramustine with mitoxantrone plus prednisone in men with metastatic , hormone-independent prostate cancer . METHODS We r and omly assigned 770 men to one of two treatments , each given in 21-day cycles : 280 mg of estramustine three times daily on days 1 through 5 , 60 mg of docetaxel per square meter of body-surface area on day 2 , and 60 mg of dexamethasone in three divided doses before docetaxel , or 12 mg of mitoxantrone per square meter on day 1 plus 5 mg of prednisone twice daily . The primary end point was overall survival ; secondary end points were progression-free survival , objective response rates , and post-treatment declines of at least 50 percent in serum prostate-specific antigen ( PSA ) levels . RESULTS Of 674 eligible patients , 338 were assigned to receive docetaxel and estramustine and 336 to receive mitoxantrone and prednisone . In an intention-to-treat analysis , the median overall survival was longer in the group given docetaxel and estramustine than in the group given mitoxantrone and prednisone ( 17.5 months vs. 15.6 months , P=0.02 by the log-rank test ) , and the corresponding hazard ratio for death was 0.80 ( 95 percent confidence interval , 0.67 to 0.97 ) . The median time to progression was 6.3 months in the group given docetaxel and estramustine and 3.2 months in the group given mitoxantrone and prednisone ( P<0.001 by the log-rank test ) . PSA declines of at least 50 percent occurred in 50 percent and 27 percent of patients , respectively ( P<0.001 ) , and objective tumor responses were observed in 17 percent and 11 percent of patients with bidimensionally measurable disease , respectively ( P=0.30 ) . Grade 3 or 4 neutropenic fevers ( P=0.01 ) , nausea and vomiting ( P<0.001 ) , and cardiovascular events ( P=0.001 ) were more common among patients receiving docetaxel and estramustine than among those receiving mitoxantrone and prednisone .  Pain relief was similar in both groups . CONCLUSIONS The improvement in median survival of nearly two months with docetaxel and estramustine , as compared with mitoxantrone and prednisone , provides support for this approach in men with metastatic , and rogen-independent prostate cancer Sub clinical prostate cancer is common in men > 50 years of age .  Population -based screening of men aged between 55 and 69 years , using prostate-specific antigen ( PSA ) testing , has been evaluated in r and omised trials [ 1 , 2 ] . After a median follow-up of 13 years , the European screening trial demonstrated a relative reduction in the risk of prostate cancer mortality of 21 % ( 29 % if adjusted for non-compliance ) . However , 781 men needed to be invited for screening and 27 patients needed to be treated to prevent one death  from prostate cancer . Risk-adapted early detection of prostate cancer using a baseline PSA level has also been evaluated in retrospective cohort studies . The baseline PSA at or before the age of 50 years is associated with the risk of prostate cancer mortality over the subsequent 25 years [ 3 ] 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 Patients with locally advanced and high-risk prostate cancer ( LAPC ) are prone to experience biochemical recurrence despite radical prostatectomy ( RP ) . We evaluated feasibility , safety and activity of a neoadjuvant chemohormonal therapy ( NCHT ) with 3-weekly full dose docetaxel and complete and rogen blockade ( CAB ) in locally advanced and high-risk prostate cancer patients ( LAPC ) undergoing RP . Methods Patients ( n = 30 ) were selected by Kattans ’ preoperative score and received trimestral buserelin 9,45 mg , bicalutamide 50 mg/day and 3 cycles docetaxel ( 75 mg/m2 ) followed by RP . Primary endpoints were biochemical ( PSA ) and local downstaging . Secondary endpoints included toxicity and operability assessment s , pathological complete response ( pCR 
    Output:
    
The combination of ADT with docetaxel improved the clinical progression-free survival , bPFS and OS of patients with mHNPC .
A superior OS was seen especially for patients with metastatic and high-volume disease .