***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 This pivotal phase III trial evaluated the efficacy and safety of palonosetron in preventing acute and delayed chemotherapy-induced nausea and vomiting ( CINV ) following highly emetogenic chemotherapy ( HEC ) . PATIENTS AND METHODS Patients were r and omized to a single intravenous dose of palonosetron 0.25 mg or 0.75 mg , or ondansetron 32 mg prior to HEC . Dexamethasone pre-treatment ( with stratification ) was used at investigator discretion . The primary efficacy endpoint was the proportion of patients with  complete response ( CR ) during the first 24 h post-chemotherapy ( acute phase ) . RESULTS In the intent-to-treat analysis ( n = 667 ) , palonosetron 0.25 mg and 0.75 mg were at least as effective as ondansetron in preventing acute CINV ( 59.2 % , 65.5 % , and 57.0 % CR rates , respectively ) ; CR rates were slightly higher with palonosetron than ondansetron during the delayed ( 24 - 120 h ) and overall ( 0 - 120 h ) phases . Two thirds of patients ( n = 447 ) received concomitant dexamethasone . Patients pre-treated with palonosetron 0.25 mg plus dexamethasone had significantly higher CR rates than those receiving ondansetron plus dexamethasone during the delayed ( 42.0 % versus 28.6 % ) and overall ( 40.7 % versus 25.2 % ) phases . Palonosetron and ondansetron were well tolerated . CONCLUSIONS Single-dose palonosetron was as effective as ondansetron in preventing acute CINV following HEC , and with dexamethasone pre-treatment , its effectiveness was significantly increased over ondansetron throughout the 5-day post-chemotherapy period Although elderly patients have been reported to be less prone to chemotherapy-induced nausea and vomiting ( CINV ) , its management is complicated by a high frequency of comorbidities and polypharmacy and an increased risk of dehydration and impaired cognition . The comparative efficacy and tolerability of palonosetron and ondansetron/dolasetron were assessed in a retrospective post hoc analysis using pooled data from 171 elderly patients ( age > or = 65 years ) with cancer enrolled in two r and omized , double-blind , phase III clinical studies comparing single IV doses of these antiemetic agents given prior to receipt of moderately emetogenic chemotherapy  . The complete response rate during the postchemotherapy period was significantly higher in the palonosetron group than in the ondansetron/dolasetron group in the 5 days following chemotherapy . The proportion of patients who were nausea-free on the problematic days 2 and 3 post chemotherapy and the time to treatment failure also significantly favored palonosetron . In this population that included patients with pre-existing comorbidities , palonosetron was well tolerated , with similar or fewer adverse events than the comparators . Comparisons in electrocardiogram parameters revealed that the mean postdose change from baseline in QTc interval was 3 ms for palonosetron 0.25 mg and 5 ms for ondansetron/dolasetron . In this retrospective analysis , palonosetron provided superior efficacy to ondansetron/dolasetron for the treatment of CINV in elderly patients receiving moderately emetogenic chemotherapy . Based on its safety profile , antiemetic control , and convenient dosing , palonosetron can be recommended for use in elderly patients with cancer receiving emetogenic chemotherapy BACKGROUND The localization of substance P in brain-stem regions associated with vomiting , and the results of studies in ferrets , led us to postulate that a neurokinin-1-receptor antagonist would be an antiemetic in patients receiving anticancer chemotherapy . METHODS In a multicenter , double-blind , placebo-controlled trial involving 159 patients who had not previously received cisplatin  , we evaluated the prevention of acute emesis ( occurring within 24 hours ) and delayed emesis ( on days 2 to 5 ) after a single dose of cisplatin therapy ( 70 mg or more per square meter of body-surface area ) . Before receiving cisplatin , all the patients received granisetron ( 10 microg per kilogram of body weight intravenously ) and dexamethasone ( 20 mg orally ) . The patients were r and omly assigned to one of three treatments in addition to granisetron and dexamethasone : 400 mg of an oral trisubstituted morpholine acetal ( also known as L-754,030 ) before cisplatin and 300 mg on days 2 to 5 ( group 1 ) , 400 mg of L-754,030 before cisplatin and placebo on days 2 to 5 ( group 2 ) , or placebo before cisplatin and placebo on days 2 to 5 ( group 3 ) . Additional medication was available at any time to treat occurrences of vomiting or nausea . RESULTS In the acute-emesis phase , 93 percent of the patients in groups 1 and 2 combined and 67 percent of those in group 3 had no vomiting ( P<0.001 ) . In the delayed-emesis phase , 82 percent of the patients in group 1 , 78 percent of those in group 2 , and 33 percent of those in group 3 had no vomiting ( P<0.001 for the comparison between group 1 or 2 and group 3 ) . The median nausea score in the delayed-emesis phase was significantly lower in group 1 than in group 3 ( P=0.003 ) . No serious adverse events were attributed to L-754,030 . CONCLUSIONS The neurokinin-1-receptor antagonist L-754,030 prevents delayed emesis after treatment with cisplatin . Moreover , combining L-754,030 with granisetron plus dexamethasone improves the prevention of acute emesis Palonosetron , a highly selective and potent 5‐HT3 receptor antagonist with a strong binding affinity and a long plasma elimination half‐life ( approximately 40 hours ) , has shown efficacy in Phase II trials in preventing  chemotherapy‐induced nausea and vomiting ( CINV )  result ing from highly emetogenic chemotherapy . The current Phase III trial evaluated the efficacy and safety of palonosetron in preventing acute and delayed CINV after moderately emetogenic chemotherapy 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 
    Output:
    
Patients receiving PAL presented less nausea and vomiting regardless of the use of corticoids .
Conclusion PAL was more effective than the other 5-HT3R in preventing acute and delayed CINV in patients receiving MoHE treatments , regardless of the use of concomitant corticosteroids