***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: & NA ; The objective was to investigate the relationship between pain relief scores produced by placebo and by active interventions in r and omised controlled trials ( RCTs ) . Individual patient categorical pain relief scores from 5 placebo‐controlled single‐dose parallel‐group RCTs in acute postoperative pain were used to calculate the percentage of the maximum possible pain relief score ( % maxTOTPAR ) for the different treatments . One hundred and thirty of the 525 patients in the 5 trials had a placebo .  Individual patients ' scores with placebo varied from 0 to 100 % of the maximum possible pain relief . The proportion who obtained more than 50 % of the maximum possible pain relief with placebo varied from 7 % to 37 % across the trials ; with the active drugs the variation was from 5 to 63 % . Mean placebo scores were related to the mean score for the active treatments in each study ; the higher the mean active score , the higher the mean placebo score . This relationship disappeared when median values were used . Medical folklore has it that the amount of relief obtained with placebo is one‐third of the maximum possible ( and does not vary ) , and that one‐third of patients respond to placebo . The results show that the amount of relief obtained with placebo varies considerably between patients , that 38 % of patients obtained more than 10 % of the maximum possible relief , and 16 % obtained greater than 50 % . In double‐blind , r and omised parallel‐group studies of high quality placebo scores should not vary . Despite these conditions being met the placebo scores did vary . The previous explanation , of a relationship between the mean placebo scores and the mean scores for the active treatments was not supported 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 Abstract A previously established relationship for deriving dichotomous from continuous information in r and omised controlled trials ( RCTs ) of analgesics has been tested using an independent data set . Individual patient information from 18 RCTs of parallel‐group design in acute postoperative pain ( after abdominal , gynaecological and oral surgery ) was used to calculate the percentage of the maximum possible pain relief score  ( % maxTOTPAR ) and the proportion of patients with > 50%maxTOTPAR for the different treatments . The relationship between the measures was investigated in 85 treatments with over 3400 patients . In 80 of 85 treatments ( 94 % ) agreement between calculated and actual number of patients with > 50%maxTOTPAR was within four patients per treatment and in 72 ( 85 % ) was within three ( average of 40 patients per treatment , range 21–58 patients ) . Summing the positive and negative differences between actual and calculated numbers of patients with > 50%maxTOTPAR gave an average difference of 0.30 patients per treatment arm . Reports of RCTs of analgesics frequently describe results of studies in the form of mean derived indices , rather than using discontinuous events , such as number or proportion of patients with 50 % pain relief . Because mean data inadequately describe information with a non‐normal distribution , combining mean data in systematic review s may compromise the results . Showing that dichotomous data can reliably be derived from mean data in acute pain studies enables data published as means to be used for quantitative systematic review s which require data in dichotomous form & NA ; Reports of RCTs of analgesics frequently describe results of studies in the form of mean derived indices , rather than using discontinuous events — such as number or proportion of patients with 50 % pain relief . Because mean data inadequately describe information with a non‐normal distribution , combining mean data in systematic review s may compromise the results . Showing that dichotomous data can reliably be derived from mean data , at least in acute pain models , indicates that more meaningful overviews or meta‐ analysis may be possible . This study investigated the relationship between continuous and dichotomous analgesic measures in a set of individual patient data , and then used that relationship to derive dichotomous from continuous information in r and omised controlled trials ( RCTs ) of analgesics .  Individual patient information from 13 RCTs of parallel‐group and crossover design in acute postoperative pain was used to calculate the percentage of the maximum possible pain relief score ( % maxTOTPAR ) and the proportion of patients with greater than 50 % pain relief ( > 50%maxTOTPAR ) for the different treatments . The relationship between the measures was investigated in 45 actual treatments and 10 000 treatments simulated using the underlying actual distribution ; 1283 patients had 45 separate treatments . Mean % maxTOTPAR correlated with the proportion of patients with > 50%maxTOTPAR ( r2 = 0.90 ) . The relationship calculated from all the 45 treatments predicted to within three patients the number of patients with more than 50 % pain relief in 42 of 45 treatments , and 
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RESULTS -ANALGESIA , INEFFECTIVE INTERVENTIONS : There is good evidence that some interventions are ineffective .
RESULTS -ANALGESIA , INTERVENTIONS OF DOUBTFUL VALUE : Some interventions may be effective but the size of the effect or the complication of undertaking them confers no measurable benefit over conventional methods .
Such interventions include : ( 1 ) injecting morphine into the knee joint after surgery : there is a small analgesic benefit which may last for up to 24 hours but there is no clear evidence that the size of the benefit is of any clinical value ; ( 2 ) manoeuvres to try and anticipate pain by using pre-emptive analgesia ; these are no more effective than st and ard methods ; ( 3 ) administering non-steroidal anti-inflammatory drugs ( NSAIDs ) by injection or per rectum in patients who can swallow ; this appears to be no more effective than giving NSAIDs by mouth and , indeed , may do more harm than good ; ( 4 ) administering codeine in single doses ; this has poor analgesic efficacy .
RESULTS -ANALGESIA , INTERVENTIONS OF PROVEN VALUE : These include a number of oral analgesics including ( at st and ard doses ) : ( 1 ) dextropropoxyphene ; ( 2 ) tramadol ; ( 3 ) paracetamol ; ( 4 ) ibuprofen ; ( 5 ) diclofenac .
Diclofenac and ibuprofen at st and ard doses give analgesia equivalent to that obtained with 10 mg of intramuscular morphine .
Each will provide at least 50 % pain relief from a single oral dose in patients with moderate or severe postoperative pain .
Paracetamol and codeine combinations also appear to be highly effective , although there is little information on the st and ard doses used in the UK .
Dose-response relationships show that higher doses of ibuprofen may be particularly effective .
Topical NSAIDs ( applied to the skin ) are effective in minor injuries and chronic pain but there is no obvious role for them in day-case surgery .
RESULTS -POSTOPERATIVE NAUSEA AND VOMITING : The proportion of patients who may feel nauseated or vomit  after surgery is very variable , despite similar operations and anaesthetic techniques .