***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: Seventy-two patients complaining of abdominal pain were studied in a double blind trial with otilonium bromide ( OB ) ( 40 mg tid or placebo ) . In our patients we performed , before and after the treatment , a clinical evaluation ( symptom variations ) and functional studies ( sigmoid manometry during bowel distension ) . As regards clinical parameters , otilonium bromide significantly reduced abdominal pain and bloating and significantly increased ( p less than 0.02 ) the pain threshold . However the comparison with the placebo group did not show any difference between the two groups . Sigmoid motility during distension was significantly reduced ( p less than 0.05 ) in OB group , whereas it did not change in the placebo group . We can conclude that , in irritable bowel syndrome ( IBS ) patients , OB is able to improve symptoms and to reduce stimulated motor activity of the sigmoid A double-blind controlled therapeutic trial of factorial design was used to study the therapeutic effects of lorazepam , hyoscine butylbromide , and ispaghula husk in 12 r and omised blocks of eight patients with the irritable bowel syndrome ( IBS ) . Each of the three agents caused a sustained symptomatic improvement in some of the patients , although only with ispaghula was the difference between the real and dummy preparation statistically significant . When the eight possible combinations of treatment were analysed none of the 12 patients who received only dummy preparations of the three agents had maintained any improvement over the three months of the trial . Seven patients improved among the 12 who received potent preparations of all three agents , and between four and six patients improved in the groups receiving one or two of the potent preparations . These therapeutic results , though far from perfect , show that the types of drug commonly used to treat IBS are of some value and may be additive in their effects . Similar combinations of other therapeutic agents may be more effective , but it will be possible to determine this only by carrying out factorial therapeutic trials 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 : Alverine citrate has been used in the treatment of  irritable bowel syndrome  for many years Methods for combining data from several studies exist and appear to be quite useful . None satisfactorily addresses the question of what studies should be combined . This issue is the most serious method ological limitation . Even studies with statistically significant interaction might still be combined if the effect were in the same direction . Thus , substantial scientific input is required as to what criteria must be met by each potential study . Much can be learned from combining or pooling data but it must be done cautiously . Pooling exercises do not replace well design ed prospect i ve clinical trials . Efforts for establishing basic design criteria to allow for multicentre and multicountry trials to be more easily combined might be useful . To evaluate the efficacy of otilonium bromide , a spasmolytic agent , in the treatment of  irritable bowel syndrome  using modern and vali date d diagnostic criteria BACKGROUND & AIMS Irritable bowel syndrome ( IBS ) is subtyped as IBS with diarrhea ( IBS-D ) or IBS with constipation ( IBS-C ) based on Rome II guidelines . The remaining group is considered as having mixed IBS ( IBS-M ) . There is no st and ard definition of an alternator ( IBS-A ) , in which bowel habit changes over time . Our aim was to use Rome II criteria to prospect ively assess change in bowel habit for more than 1 year to underst and IBS-A. METHODS Female patients ( n=317 ) with IBS entering a National Institutes of Health treatment trial were studied at baseline with question naires and 2-week daily diary cards of pain and stool frequency and consistency . Studies were repeated at the end of treatment ( 3 months ) and at four 3-month intervals for one more year . Algorithms to classify subjects into IBS-D , IBS-C , and IBS-M groups used diary card information and modified Rome II definitions . Changes in bowel habit at 3-month intervals were then assessed using these surrogate diary card measures . RESULTS At baseline , 36 % had IBS-D , 31 % IBS-M , and 34 % IBS-C. Except for stool frequency , there were no differences between groups . While the proportion of subjects in each subgroup remained the same over the year , most individuals ( more than 75 % ) changed to either of the other 2 subtypes at least once . IBS-M was the least stable ( 50 % changed out by 12 weeks ) . Patients were more likely to transition between IBS-M and IBS-C than between IBS-D and IBS-M. Notably , only 29 % switched between the IBS-D and IBS-C subtypes over the year . CONCL 
    Output:
    
Antispasmodics were more effective than placebo in IBS , without any significant adverse events .
The addition of simethicone improved the properties of the antispasmodic agents , as seen with the alverine/simethicone and pinaverium/simethicone combinations