title: Surgery of the tonsils and adenoids in relation to secretory otitis media in children. Size is everything – large amounts of information are needed to overcome random effects in estimating direction and magnitude of treatment effects The importance of quality of primary studies in producing unbiased systematic reviews. Risk factors for chronicity in childhood otitis media with effusion. Using Numerical Results from Systematic Reviews in Clinical Practice Confidence intervals rather than P values: estimation rather than hypothesis testing. Randomised controlled trial of early surgery versus watchful waiting for glue ear: the effect on behavioural problems in pre-school children. The role of Mucodyne in reducing the need for surgery in patients with persistent otitis media with effusion. Clinical evaluation of S-CMC syrup applied in the treatment of otitis media with effusion. Double blind comparative test with placebo. Management of middle-ear effusions in children.
abstract: One hundred and fifty children aged between 2 and 9 years with bilateral SOM were allocated at r and om to three groups : Adeno-tonsillectomy ( 47 cases ) , adenoidectomy ( 47 cases ) and neither procedure ( 53 cases ) . A further 42 children were allocated to either adenoidectomy or no surgery . In all only one ear was treated at r and om by myringotomy and insertion of a ventilation tube . The unoperated ear was examined six weeks , six months , nine months , one , two and three years post-operatively for otoscopic clearance of the effusion , impedance change and improvement in mean hearing threshold . Thus the effects on middle ear status of adenoidectomy alone and in combination with tonsillectomy were assessed . There was a significant clearance of middle ear fluid , change from no peak to peak impedance measurement and hearing gain as a result of both procedures with a late trend favouring the combined operation . Neither pre-operative tonsils size nor the number of attacks of tonsillitis affect outcome . However there is a relationship between age and radiographic post-nasal space airway size with clearance of the effusion one year after surgery . These findings permit recommendation for adenoidectomy with improved expectation for resolution of SOM if cases are selected with reference to these two variables . They also suggest in which cases the operation should not be recommended . Despite the trends in outcome favouring combined operations , the extra morbidity associated with the addition of tonsillectomy does not at present justify recommendation for removal of the tonsils in addition to adenoidectomy for cases with SOM . Tonsillectomy in these children should be assessed on grounds of tonsillar pathology per se and not on the status of the middle ear Abstract  Variability  in patients ' response to interventions in pain and other clinical setting s is large . Many explanations such as trial methods , environment or culture have been proposed , but this paper sets out to show that the main cause of the variability may be r and om chance , and that if trials are small their estimate of magnitude of effect may be incorrect , simply because of the r and om play of chance . This is highly relevant to the questions of ‘ How large do trials have to be for statistical accuracy ? ’ and ‘ How large do trials have to be for their results to be clinical ly valid ? ’ The true underlying control event rate ( CER ) and experimental event rate ( EER ) were determined from single‐dose acute pain analgesic trials in over 5000 patients . Trial group size required to obtain statistically significant and clinical ly relevant ( 0.95 probability of number‐needed‐to‐treat within ±0.5 of its true value ) results were computed using these values . Ten thous and trials using these CER and EER values were simulated using varying group sizes to investigate the variation due to r and om chance alone . Most common analgesics have EERs in the range 0.4–0.6 and CER of about 0.19 . With such efficacy , to have a 90 % chance of obtaining a statistically significant result in the correct direction requires group sizes in the range 30–60 . For clinical relevance nearly 500 patients are required in each group . Only with an extremely effective drug ( EER>0.8 ) will we be reasonably sure of obtaining a clinical ly relevant NNT with commonly used group sizes of around 40 patients per treatment arm . The simulated trials showed substantial variation in CER and EER , with the probability of obtaining the correct values improving as group size increased . We contend that much of the variability in control and experimental event rates is due to r and om chance alone . Single small trials are unlikely to be correct . If we want to be sure of getting correct ( clinical ly relevant ) results in clinical trials we must study more patients . Credible estimates of clinical efficacy are only likely to come from large trials or from pooling multiple trials of conventional ( small ) size BACKGROUND Traditional and largely qualitative review s of evidence are now giving way to much more structured systematic overviews that use a quantitative method to calculate the overall effect of treatment . The latter approach is dependent on the quality of primary studies , which may introduce bias if they are of poor method ologic quality . OBJECTIVE To test the hypothesis that the inclusion of poor- quality trials in meta-analyses would bias the conclusions and produce incorrect estimates of treatment effect . METHODS An overview of r and omized trials of antiestrogen therapy in subfertile men with oligospermia was performed to test the hypothesis . Data sources included online search ing of MEDLINE and Science Citation Index data bases between 1966 and 1994 , scanning the bibliography of known primary studies and review articles , and contacting experts in the field . After independent , blind assessment , nine of 149 originally identified studies met the inclusion criteria and were selected . We assessed study quality independently . Outcome data from each study were pooled and statistically summarized . RESULTS There was a marginal improvement in pregnancy rate with antiestrogen treatment ( odds ratio , 1.6 ; 95 % confidence interval , 0.9 to 2.6 ) . Sensitivity analyses on the basis of method ologic quality demonstrated that poor- quality studies produced a positive effect with treatment , whereas no benefit was observed with high- quality studies . CONCLUSION The results of a meta- analysis are influenced by the quality of the primary studies included . Method ologically , poor studies tend to exaggerate the overall estimate of treatment effect and may lead to incorrect inferences One hundred and ninety-one children who presented to the author 's clinic with otitis media with effusion ( OME ) over a 2-year period ( 1986 - 1988 ) have been studied prospect ively . Resolution of their OME was considered to have occurred when they had normal findings on  pneumatic otoscopy , tympanometry and audiometry on at least two occasions 6 months apart and had subjectively normal hearing in between . The number of operations performed during the course of the disease was used as a surrogate for chronicity .  Children with a history of previous aural discharge at presentation were significantly more likely to have chronic OME ( P = < 0.02 ) 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 Overemphasis on hypothesis testing-- and the use of P values to dichotomise significant or non-significant results --has detracted from more useful approaches to interpreting study results , such as estimation and confidence intervals . In medical studies investigators are usually interested in determining the size of difference of a measured outcome between groups , rather than a simple indication of whether or not it is statistically significant . Confidence intervals present a range of values , on the basis of the sample data , in which the population value for such a difference may lie . Some methods of calculating confidence intervals for means and differences between means are given , with similar information for proportions . The paper also gives suggestions for graphical display . Confidence intervals , if appropriate to the type of study , should be used for major findings in both the main text of a paper and its abstract This trial was design ed to compare early surgery with watchful waiting for persistent bilateral otitis media with effusion ( OME ) in terms of hearing loss and behavioural problems in pre-school children . A r and omised controlled trial compared the effects of early treatment with ventilation tubes versus watchful waiting for 9 months . Results were analysed by intention-to-treat . One hundred and eighty-two pre-school children ( date of birth between 1 April 1991 and 31 December 1992 ) , mean age of 2.9 years ( SD 0.85 ) with at least a 3-month history of bilateral OME and hearing loss of greater than 25 dB were treated in Bristol Children 's Hospital between November 1993 and January 1996 .  Bilateral ventilation tubes were inserted within 6 weeks of r and omisation or within 6 weeks of re assessment after 9 months of watchful waiting , with a final assessment at 18 months . The main outcome measures were behavioural problems , measured by the Richman Behaviour Checklist , and hearing loss at 4000 Hz in the better hearing ear . Early surgical intervention significantly reduced behavioural problems by 17 % ( 95 % CI , 2 % - 33 % ) . This difference was largely mediated by concurrent hearing loss . After 18 months , there was no longer a significant difference ( 95 % Cl , -19 % to + 10 % ) . However , the majority ( 85 % ) of the Watchful Waiting group had required surgery and 22 % of all children still had behavioural problems UNLABELLED A recent meta- analysis suggested a possible beneficial effect of carboxymethylcysteine ( Mucodyne ) in resolving  otitis media with effusion ( OME ) , but the methodology in several of the included trials was flawed . A double-blind r and omised controlled trial ( RCT ) involving 163 patients ( 78 r and omised to Mucodyne and 85 to placebo )  was therefore performed . MAIN OUTCOME MEASURE operative intervention or not . Of the 28 patients with resolved OME  , 17 were in the Mucodyne group and 11 in the placebo group . Although it appeared that patients treated with Mucodyne were 1.68 times more likely to undergo resolution of OME than patients receiving placebo , this did not reach statistical significance . [ Risk ratio of 1.68 ( 95 % C.I. , 0.74 - 3.37 ) ] . chi2 test ( df = 162 ) = 2.24 ( P = 0.134 ) . The absolute risk difference in the study was 8.5 % ( 95 % C.I. , -3 - 20 ) . We can not exclude the possibility that Mucodyne is as beneficial as a 20 % additional resolution of OME , or as harmful as a 3 % decrease in the resolution of OME We carried out a double blind comparative trial with placebo serving as control using 250 cases of infant otitis media with effusion ( OME ) in order to objective ly evaluate the clinical usefulness of S-carboxymethylcysteine ( S-CMC ) , an oral mucolytic agent , against OME . The results obtained were as follows . 1 ) Global improvement rate , when judged in items of overall improvement , was 79.8 % in S-CMC group and 58.2 % in placebo group , S group being significantly better when compared to P. 2 ) The degree of improvement in each item for evaluation revealed significantly better results in S group compared to P , in both objective findings of the effusion amount and its property , and in audiological findings . 3 ) Adverse reactions observed in 3 cases ( 2.5 % ) in S group and in 2 ( 1.5 % ) in P group were not serious , and were eliminated through suspension or discontinuation of the medication . The results obtained suggested the usefulness of S-CMC when used in the treatment of OME A double-blind placebo controlled trial of Mucodyne ( carbocisteine , Berk Pharmaceuticals ) , Actifed ( triprolidine HC1 and pseudoephedrine HC1 , Wellcome ) and combined Mucodyne and Actifed in the treatment of middle-ear effusions is reported . The trial was undertaken to assess whether either preparation , alone or in combination , would reduce the number of children requiring surgical treatment for this condition . No statistical difference between the various groups in avoiding surgical treatment was detected . In those patients undergoing surgery , pre-operative treatment with Mucodyne was associated with a significantly greater number of ears restored to a normal appearance and middle ear function as measured by tympanometry . All patients relapsing after surgery belonged to the groups receiving placebo , Actifed or the combination of Mucodyne and Actifed prior to the operation
target: ConclusionS -carboxymethylcysteine is effective in the treatment of children with glue ear .
background: Objective To establish the clinical relevance of S-carboxymethylcysteine in the treatment of glue ear in children using measures approximating those saving a child from operation for grommet insertion .