***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 Supporting 21st century health care and the practice of evidence -based medicine ( EBM ) requires ubiquitous access to clinical information and to knowledge-based re sources to answer clinical questions . Many questions go unanswered , however , due to lack of skills in formulating questions , crafting effective search strategies , and accessing data bases to identify best levels of evidence . Methods This r and omized trial was design ed as a pilot study to measure the relevancy of search results using three different interfaces for the PubMed search system . Two of the search interfaces utilized a specific framework called PICO , which was design ed to focus clinical questions and to prompt for publication type or type of question asked . The third interface was the st and ard PubMed interface readily available on the Web . Study subjects were recruited from interns and residents on an inpatient general medicine rotation at an academic medical center in the US . Thirty-one subjects were r and omized to one of the three interfaces , given 3 clinical questions , and asked to search PubMed for a set of relevant articles that would provide an answer for each question . The success of the search results was determined by a precision score , which compared the number of relevant or gold st and ard articles retrieved in a result set to the total number of articles retrieved in that set . Results Participants using the PICO templates ( Protocol A or Protocol B ) had higher precision scores for each question than the participants who used Protocol C , the st and ard PubMed Web interface . ( Question 1 : A = 35 % , B = 28 % , C = 20 % ; Question 2 : A = 5 % , B = 6 % , C = 4 % ; Question 3 : A = 1 % , B = 0 % , C = 0 % ) 95 % confidence intervals were calculated for the precision for each question using a lower boundary of zero . However , the 95 % confidence limits were overlapping , suggesting no statistical difference between the groups . Conclusion Due to the small number of search es for each arm , this pilot study could not demonstrate a statistically significant difference between the search protocol s. However there was a trend towards higher precision that needs to be investigated in a larger study to determine if PICO can improve the relevancy of search results Purpose To assess physician decisions about ICU admission for life-sustaining treatments ( LSTs ) . Methods Observational simulation study of physician decisions for patients aged ≥80 years . Each patient was allocated at r and om to four physicians who made decisions based on actual bed availability and existence of an additional bed before and after obtaining information on patient preferences . The simulations involved non-invasive ventilation ( NIV ) , invasive mechanical ventilation ( IMV ) , and renal replacement therapy after a period of IMV ( RRT after IMV ) . Results The physician participation rate was 100/217 ( 46 % ) ; males without religious beliefs predominated , and median ICU experience was 9 years . Among participants , 85.7 , 78 , and 62 % felt that NIV , IMV , or RRT ( after IMV ) was warranted , respectively . By logistic regression analysis , factors associated with admission were age < 85 years , self-sufficiency , and bed availability for NIV and IMV . Factors associated with IMV were previous ICU stay ( OR 0.29 , 95 % CI 0.13–0.65 , p = 0.01 ) and cancer ( OR 0.23 , 95 % CI 0.10–0.52 , p = 0.003 ) , and factors associated with RRT ( after IMV ) were living spouse ( OR 2.03 , 95 % CI 1.04–3.97 , p = 0.038 ) and respiratory disease ( OR 0.42 , 95 % CI 0.23–0.76 , p = 0.004 ) . Agreement among physicians was low for all LSTs . Knowledge of patient preferences changed physician decisions for 39.9 , 56 , and 57 % of patients who disagreed with the initial physician decisions for NIV , IMV , and RRT ( after IMV ) respectively . An additional bed increased admissions for NIV and IMV by 38.6 and 13.6 % , respectively . Conclusions Physician decisions for elderly patients had low agreement and varied greatly with bed availability and knowledge of patient preferences Objective : Life and death triage decisions are made daily by intensive care unit physicians . Scoring systems have been developed for prognosticating intensive care unit mortality but none for intensive care unit triage . The objective of this study was to develop an intensive care unit triage decision rule based on 28-day mortality rates of admitted and refused patients . Design : Prospect i ve , observational study of triage decisions from September 2003 until March 2005 . Setting : Eleven intensive care units in seven European countries . Patients : All patients > 18 yrs with a request for intensive care unit admission . Interventions : Admission or rejection to an intensive care unit . Measurements and Main Results : Clinical , laboratory , and physiological variables and data from severity scores were collected . Separate scores for accepted and rejected patients with 28-day mortality end point were built . Values for variables were grouped into categories determined by the locally weighted least squares graphical method applied to the logit of the mortality and by univariate logistic regressions for reducing c and i date s for the score . Multivariate logistic regression was used to construct the final score . Cutoff values for 99.5 % specificity were determined . Of 6796 patients , 5602 were admitted and 1194 rejected . The initial refusal score included age , diagnosis , systolic blood pressure , pulse , respirations , creatinine , bilirubin , PaO2 , bicarbonate , albumin , use of vasopressors , Glasgow Coma Scale score , Karnofsky Scale , operative status and chronic disorder , and the initial refusal receiver operating characteristics were area under the curve 0.77 ( 95 % confidence interval 0.76–0.79 ) . The final triage score included age , diagnosis , creatinine , white blood cells , platelets , albumin , use of vasopressors , Glasgow Coma Scale score , Karnofsky Scale , operative status and chronic disorder , and the final score receiver operating characteristics were area under the curve 0.83 ( 95 % confidence interval 0.80–0.86 ) . Patients with initial refusal scores > 173.5 or final triage scores = 0 should be rejected . Conclusions : The initial refusal score and final triage score provide objective data for rejecting patients that will die even if admitted to the intensive care unit and survive if refused intensive care unit admission . ( Crit Care Med 2012 ; 40:125–131 Abstract . Objectives : To evaluate factors associated with decisions to refuse ICU admission and to assess the outcome of refused patients . Design and setting : Prospect i ve , descriptive evaluation in a multi-disciplinary intensive care unit , university referral hospital . Patients and participants : All adult emergency referrals over a 7-month period . Interventions : The number of beds available at the time of referral , the patient 's age , gender , diagnosis , mortality probability model score and hospital survival were documented . The outcome of the  referral  and the reason for refusal were recorded . Measurements and results : Of 624 patients 388 were admitted and 236 ( 38 % ) refused . Reasons for refusal were triage ( n=104 ) , futility ( n=82 ) and inappropriate referral ( too well ; n=50 ) . The st and ardised mortality ratio ( SMR ) for refused and admitted groups was 1.24 ( 95 % CI 1.05–1.46 ) and 0.93 ( 0.78–1.09 ) respectively . The SMR ratio ( refused SMR/admitted SMR ) was highest in the middle range of illness ( 1.95 , 1.19–3.20 ) . Inappropriate referrals had a better than expected outcome despite refusal , with a SMR ratio of 0.39 ( 0.11–0.99 ) . Excluding inappropriate referrals , multivariate analysis demonstrated that refusal was associated with older age , diagnostic group and severity of illness . Triage decisions were associated with a diagnosis of sepsis , and futility decisions with greater severity of illness and recent cardiac arrest . Conclusions : Refusal of admission to our ICU is common . Excess mortality of patients refused is most marked in the middle range of severity of illness . Age , diagnostic group , and severity of illness are important in decision making . Strategies should be developed to create admission criteria that would identify patients in the middle range of severity of illness who should benefit most from ICU care Purpose To report determinants and outcomes associated with decisions to deny or to delay intensive care unit ( ICU ) admission in critically ill patients . Methods An observational prospect i ve study over a 6-month period . All adult patients triaged for admission to a medical ICU were included prospect ively . Age , gender , reasons for requesting ICU admission , severity of underlying disease , severity of acute illness , mortality and ICU characteristics were recorded . Multinomial logistic regression analysis was used for evaluating predicting factors of refused ICU admission . Results ICU admission was requested for 398 patients : 110 were immediately admitted ( 27.8 % ) , 142 were never admitted ( 35.6 % ) , and 146 were admitted at a later time ( 36.6 % ) . The reasons for refusal were : too sick to benefit ( 31 , 10.8 % ) , too well to benefit ( 55 , 19.1 % ) , unit full ( 117 , 40.6 % ) , and more data about the patient were needed to make a decision ( 85 , 29.5 % ) . Multivariate analysis revealed that late ICU admission was associated with the lack of available ICU beds ( OR 1.91 ; 95 % CI 1.46–2.50 ; p = 0.003 ) , cardiac disease ( OR 7.77 ; 95 % CI 2.41–25.04 ; p < 0.001 ) , neurological disease ( OR 3.78 ; 95 % CI 1.40–10.26 ; p = 0.009 ) , shock and sepsis ( OR 2.55 ; 95 % CI 1.06–6.13 ; p = 0.03 ) , and metabolic disease ( OR 2.84 ; 95 % CI 1.11–7.30 ; p = 0.02 ) . Factors for ICU refusal for never admitted patients were : severity of acute illness ( OR 4.83 ; 95 % CI 1.11–21.01 ; p = 0.03 ) , cardiac disease ( OR 14.26 ; 95 % CI 3.95–51.44 ; p < 0.001 ) , neurological disease ( OR 4.05 ; 95 % CI 1.33–12.28 ; p = 0.01 ) and lack of available ICU beds ( OR 6.26 ; 95 % CI 4.14–9.46 ; p < 0.001 ) . Hospital mortality was 33.3 % ( 37/110 ) for immediately admitted patients , 43.8 % ( 64/146 ) for patients admitted later and 49.3 % ( 70/142 ) for never admitted patients . Conclusion Refusal of ICU admission was correlated with the severity of acute illness , lack of ICU beds and reasons for admission request . Further efforts are needed to define which patients are most likely to benefit from ICU admission and to improve the accuracy of data on ICU refusal rates Objectives : To test whether hospital-based physicians made different intensive care unit and life-sustaining treatment decisions for otherwise identical black and white patients with end-stage cancer and life-threatening hypoxia . Design : We conducted a r and omized trial of the relationship between patient race and physician treatment decisions using high-fidelity simulation . We counterbalanced the effects of race and case by r and omly alternating their order using a table of r and om permutations . Physicians completed two simulation encounters with  black and white patient simulator patients with prognostically identical end-stage gastric or pancreatic cancer and life-threatening hypoxia and hypotension , followed by a self-administered survey of beliefs regarding treatment preferences by race . We conducted within-subjects analysis of each physician 's matched-pair simulation encounters , adjusting for order and case effects , and between-subjects analysis of physicians ' first encounter , adjusting for case . Setting : Peter M. Winter Institute for Simulation Education and Research at the University of Pittsburgh , Pennsylvania . Subjects : Thirty-three hospital-based attending physicians , including 12 emergency physicians , eight hospitalists , and 13 intensivists from Allegheny County , Pennsylvania . Intervention : Race of patient simulator . Measurements and Main Results : Measurements included physician treatment decisions recorded during the simulation and documented in the chart and beliefs about treatment preference by race . When faced with a black vs. a white patient , physicians did not differ in their elicitation of intubation preferences ( within-subject comparison , 28/32 [ 88 % ] vs. 28/32 [ 88 % ] ; p = .589 ; between-subject comparison , 13/17 [ 87 % ] vs. 13/17 [ 76 % ] ; p = .460 ) , intensive care unit admission ( within-subject comparison , 14/32 [ 44 % ] vs. 12/32 [ 38 % ] ; p = .481 ; between-subject comparison , 8/15 ( 53 % ) vs. 7/17 ( 41 % ) ; p = .456 ) , intubation ( within 
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The literature provides some indication of these factors , but fail to show complexities and interactions between them .
A decision tree is proposed .