***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: We conducted a placebo-controlled , single-blind study to determine the efficacy of a local preoperative injection of a steroid/anesthetic combination in preventing post-tonsillectomy pain . We r and omized 21 adults to receive either triamcinolone/bupivacaine on the left side and saline on the right or vice versa . Injections were administered in the area of the tonsillar pillars following intubation and prior to tonsillectomy . Based on the “ generalized estimating equations ” model of statistical analysis , we found no significant difference in the degree of postoperative pain between the active-treatment and control sides BACKGROUND We conducted a factorial study of emesis prophylaxis with ondansetron ( OND ) , metoclopramide ( MET ) , and dexamethasone ( DEX ) . METHODS After informed parental consent , 240 children having adenotonsillectomy were r and omized to one of 15 combinations of OND ( 0 - 60 microg.kg(-1 ) ) , MET ( 0 - 400 microg.kg(-1 ) ) , and /or DEX ( 0 - 500 microg.kg(-1 ) ) . Using multivariable logistic regression , models were generated for the probability of emesis before discharge , after discharge and overall for 24 h. RESULTS Odds of emesis increased by a factor of three to four for children older than 7 years . Before discharge , odds of emesis decreased by factors of 0.29 for each 15 microg.kg(-1 ) of OND and 0.37 for each 100 microg.kg(-1 ) of MET . After discharge , odds of emesis decreased by a factor of 0.67 for each 125 microg.kg(-1 ) of DEX and increased by a factor of 3.5 for emesis before discharge . Over 24 h , odds of emesis decreased with OND , MET , and DEX ( ORs as above ) . A negative interaction between OND and MET was seen before discharge and over 24 h , reducing the efficacy of their combination . CONCLUSIONS We present novel study design and methods of analysis which are uniquely suited to studies of multiple interventions . Factorial design was a powerful tool , allowing simultaneous determination of dose-response relationships for three drugs and identifying a previously unreported negative interaction between OND and MET OBJECTIVE To determine the effect of preoperative dexamethasone sodium phosphate administration on posttonsillectomy morbidity for electrocautery ( " hot " ) and sharp ( " cold " ) dissection techniques . DESIGN Prospect i ve , r and omized , double-blind study . SETTING University pediatric hospital and county teaching hospital . Subjects A total of 219 children , aged 9 months to 12 years , undergoing tonsillectomy .  Intervention Participants who underwent tonsillectomy  were r and omly assigned to receive either intravenous dexamethasone sodium phosphate ( 1 mg/kg ) or placebo . OUTCOME MEASURES Pain scores , oral intake , and emesis on postoperative day ( POD ) 1 . RESULTS A total of 106 subjects ( 62 undergoing hot and 44 cold tonsillectomies ) received preoperative steroids , and 113 ( 56 hot and 57 cold tonsillectomies ) received placebo . On POD 1 , pain scores reported by patients ( P = .02 ) , parents ( P = .002 ) , and physicians ( P<.001 ) were significantly lower in subjects receiving steroids than in those receiving placebo .  Emesis was reduced from a mean of 2.1 ( placebo group ) to 1.2 episodes ( steroid group ) ( P = .02 ) . Oral intake improved from 24.5 % of normal diet ( placebo ) to 31.7 % ( steroid group ) ( P = .004 ) . When all 4 groups were compared ( cold placebo , cold steroid , hot placebo , and hot steroid ) , pain scores reported by physicians and parents were significantly lower in the cold steroid group than in the other groups . CONCLUSIONS Perioperative dexamethasone use reduces posttonsillectomy morbidity in pediatric patients in the early postoperative period after hot or cold tonsillectomy . The combination of steroid and cold dissection technique provided the greatest advantage in reducing posttonsillectomy subjective pain levels A system has been constructed to evaluate the design , implementation , and analysis of  r and omized control trials ( RCT ) . The degree of quadruple blinding ( the r and omization process , the physicians and patients as to therapy , and the physicians as to ongoing results ) is considered to be the most important aspect of any trial . The analytic techniques are scored with the same emphasis as is placed on the control of bias in the planning and implementation of the studies . Description of the patient and treatment material s and the measurement of various controls of quality have less weight . An index of quality of a RCT is proposed with its pros and cons . If published papers were to approximate these principles , there would be a marked improvement in the quality of r and omized control trials . Finally , a reasonable st and ard design and conduct of trials will facilitate the interpretation of those with conflicting results and help in making valid combinations of undersized trials Edema contributes substantially to the postoperative discomfort and morbidity of adenotonsillectomy .  In a double-blind study , 58 children undergoing adenotonsillectomy were given a single intraoperative dose of either dexamethasone or saline . The steroid markedly affected the postoperative course in the first day after surgery . According to parental reports , the percentage of patients vomiting was reduced from 48 to 4 % . Severe throat pain was reported in 57 % of controls and only 20 % of dexamethasone patients . Twice as many steroid patients as controls tolerated some soft food on the first postoperative day . It appears that dexamethasone can greatly improve patient diet and comfort after adenotonsillectomy The past decade has seen the rise of evidence -based medicine , a movement that has focused attention on the importance of using clinical studies for empirical demonstration of the efficacy of medical interventions . Increasingly , physicians are being called on to assess such studies to help them make clinical decisions and underst and the rationale behind recommended practice s. This type of assessment requires an underst and ing of research methods that until recently was not expected of physicians . These research methods include statistical techniques used to assist in drawing conclusions . However , the methods of statistical inference in current use are not evidence -based and thus have contributed to a widespread misperception . The misperception is that absent any consideration of biological plausibility and prior evidence , statistical methods can provide a number that by itself reflects a probability of reaching erroneous conclusions . This belief has damaged the quality of scientific reasoning and discourse , primarily by making it difficult to underst and how the strength of the evidence in a particular study can be related to and combined with the strength of other evidence ( from other laboratory or clinical studies , scientific reasoning , or clinical experience ) . This results in many knowledge cl aims that do not st and the test of time ( 1 , 2 ) . A pair of articles in this issue examines this problem in some depth and proposes a partial solution . In this article , I explore the historical and logical foundations of the dominant school of medical statistics , sometimes referred to as frequentist statistics , which might be described as error-based . I explicate the logical fallacy at the heart of this system and the reason that it maintains such a tenacious hold on the minds of investigators , policymakers , and journal editors . In the second article ( 3 ) , I present an  evidence -based approach  derived from Bayesian statistical methods , an alternative perspective that has been one of the most active areas of biostatistical development during the past 20 years . Bayesian methods have started to make inroads into medical journals ; Annals , for example , has included a section on Bayesian data interpretation in its Information for Authors section since 1 July 1997 . The perspective on Bayesian methods offered here will differ somewhat from that in previous presentations in other medical journals . It will focus not on the controversial use of these methods in measuring belief but rather on how they measure the weight of quantitative evidence . We will see how reporting an index called the Bayes factor ( which in its simplest form is also called a likelihood ratio ) instead of the P value can facilitate the integration of statistical summaries and biological knowledge and lead to a better underst and ing of the role of scientific judgment in the interpretation of medical research . An Example of the Problem A recent r and omized , controlled trial of  hydrocortisone treatment for the chronic fatigue syndrome showed a treatment effect that neared the threshold for statistical significance , P=0.06 ( 4 ) . The discussion section began , hydrocortisone treatment was associated with an improvement in symptoms This is the first such study to demonstrate improvement with a drug treatment of [ the chronic fatigue syndrome ] ( 4 ) . What is remarkable about this paper is how unremarkable it is . It is typical of many medical research reports in that a conclusion based on the findings is stated at the beginning of the discussion . Later in the discussion , such issues as biological mechanism , effect magnitude , and supporting studies are presented . But a conclusion is stated before the actual discussion , as though it is derived directly from the results , a mere linguistic transformation of P=0.06 . This is a natural consequence of a statistical method that has almost eliminated our ability to distinguish between statistical results and scientific conclusions . We will see how this is a natural outgrowth of the P value fallacy . Philosophical Preliminaries To begin our exploration of the P value fallacy , we must consider the basic elements of reasoning . The process that we use to link underlying knowledge to the observed world is called inferential reasoning , of which there are two logical types : deductive inference and inductive inference . In deductive inference , we start with a given hypothesis ( a statement about how nature works ) and predict what we should see if that hypothesis were true . Deduction is objective in the sense that the predictions about what we will see are always true if the hypotheses are true . Its problem is that we can not use it to exp and our knowledge beyond what is in the hypotheses . Inductive inference goes in the reverse direction : On the basis of what we see , we evaluate what hypothesis is most tenable . The concept of evidence is inductive ; it is a measure that reflects back from observations to an underlying truth . The advantage of inductive reasoning is that our conclusions about unobserved states of nature are broader than the observations on which they are based ; that is , we use this reasoning to generate new hypotheses and to learn new things . Its drawback is that we can not be sure that what we conclude about nature is actually true , a conundrum known as the problem of induction ( 5 - 7 ) . From their clinical experience , physicians are acutely aware of the subtle but critical difference between these two perspectives . Enumerating the frequency of symptoms ( observations ) given the known presence of a disease ( hypothesis ) is a deductive process and can be done by a medical student with a good medical textbook ( Figure 1 , top ) . Much harder is the inductive art of differential diagnosis : specifying the likelihood of different diseases on the basis of a patient 's signs , symptoms , and laboratory results . The deductions are more certain and objective but less useful than the inductions . Figure 1 . The parallels between the processes of induction and deduction in medical inference ( top ) and statistical inference ( bottom ) . The identical issue arises in statistics . Under the assumption that two treatments are the same ( that is , the hypothesis of no difference in efficacy is true ) , it is easy to calculate deductively the frequency of all possible outcomes that we could observe in a study ( Figure 1 , bottom ) . But once we observe a particular outcome , as in the result of a clinical trial , it is not easy to answer the more important inductive question , How likely is it that the treatments are equivalent ? In this century , philosophers have grappled with the problem of induction and have tried to solve or evade it in several ways . Karl Popper ( 8) proposed a philosophy of scientific practice that eliminated formal induction completely and used only the deductive elements of science : the prediction and falsification components . Rudolf Carnap tried an opposite strategy to make the inductive component as logically secure as the deductive part ( 9 , 10 ) . Both were unsuccessful in producing workable models for how science could be conducted , and their failures showed that there is no method ologic solution to the problem of fallible scientific knowledge . Determining which underlying truth is most likely on the basis of the data is a problem in inverse probability , or inductive inference , that was solved quantitatively more than 200 years ago by the Reverend Thomas Bayes . He withheld his discovery , now known as Bayes theorem ; it was not divulged until 1762 , 20 years after his death ( 11 ) . Figure 2 shows Bayes theorem in words . Figure 2 . Bayes theorem , in words . As a mathematical equation , Bayes theorem is not controversial ; it serves as the foundation for analyzing games of chance and medical screening tests . However , as a model for how we should think scientifically , it is criticized because it requires assigning a prior probability to the truth of an idea , a number whose objective scientific meaning is unclear ( 7 , 10 , 12 ) . It is speculated that this may be why Reverend Bayes chose the more dire of the publish or perish options . It is also the reason why this approach has been tarred with the subjective label and has not generally been used by medical research ers . Conventional ( Frequentist ) Statistical Inference Because of the subjectivity of the prior probabilities used in Bayes theorem , scientists in the 1920s and 1930s tried to develop alternative approaches to statistical inference that used only deductive probabilities , calculated with mathematical formulas that described ( under certain assumptions ) the frequency of all possible experimental outcomes if an experiment were repeated many times ( 10 ) . Methods based on this frequentist view of probability included an index to measure the strength of evidence called the P value , proposed by R.A. Fisher in the 1920s ( 13 ) , and a method for choosing between hypotheses , called a hypothesis test , developed in the early 1930s by the mathematical statisticians Jerzy Neyman and Egon Pearson ( 14 ) . These two methods were incompatible but have become so intertwined that they are mistakenly regarded as part of a single , coherent approach to statistical inference ( 6 , 15 , 16 ) . The P Value The P value is defined as 
    Output:
    
The evidence suggests that a single intravenous dose of dexamethasone is an effective , safe and inexpensive treatment for reducing morbidity from pediatric tonsillectomy