In this task, you are given a passage which has a question and the context. You have to generate an answer to the question based on the information present in the context.

Example Input: Context: Statin therapy in nonsurgical patient populations is associated with a significant reduction in adverse cardiovascular events, including death, myocardial infarction (MI), and stroke. Recently, statin therapy was shown to be associated with a reduced incidence of postoperative mortality in patients undergoing major noncardiac vascular surgery. We investigated the influence of preoperative statin therapy on adverse outcomes after primary coronary artery bypass graft (CABG) surgery.', 'A retrospective cohort study of patients undergoing primary CABG surgery with cardiopulmonary bypass (CPB) (n=1663) between January 1, 2000 and December 31, 2001 at the Texas Heart Institute was performed. Patients were classified into 2 groups: patients receiving preoperative statin therapy (n=943) and patients not receiving preoperative antihyperlipidemic therapy (n=720). To determine if preoperative statin therapy was independently associated with a reduction in the risk of adverse postoperative outcomes, multivariate stepwise logistic regression was performed controlling for patient demographics, medical history, and preoperative medications. Multivariate logistic regression analysis demonstrated that preoperative statin therapy was independently associated with a significant reduction ( approximately 50%) in the risk of 30-day all-cause mortality (3.75% versus 1.80%; P<0.05). The adjusted odds ratio for early mortality in patients receiving preoperative statin therapy compared with patients not receiving antihyperlipidemic agents was 0.53 (95% CI, 0.28 to 0.99). Statin therapy was not independently associated with a reduced risk of postoperative MI, cardiac arrhythmias, stroke, or renal dysfunction. In an attempt to further control for selection bias related to the choice of therapy, multivariate analysis of a propensity-matched cohort of 1362 patients revealed that preoperative statin therapy was independently associated with a significant reduction in the composite endpoint of 30-day all-cause mortality and stroke (7.1% versus 4.6%; P<0.05).\Question: Are statins associated with a reduced incidence of perioperative mortality after coronary artery bypass graft surgery?
Example Output: Preoperative statin therapy may reduce the risk of early mortality after primary CABG surgery with CPB.

Example Input: Context: Radiation induces an important apoptosis response in irradiated organs. The objective of this study was to investigate the radioprotective effect of tetramethylpyrazine (TMP) on irradiated lymphocytes and discover the possible mechanism of protection.', 'Lymphocytes were pretreated for 12 h with TMP (25-200 molL(-1)) and then exposed to 4 Gy radiation. Cell apoptosis and the signaling pathway were analyzed.', 'Irradiation increased cell death, DNA fragmentation, activated caspase activation and cytochrome c translocation, downregulated B-cell lymphoma 2 (Bcl-2) and up-regulated Bcl-2-associated X protein (Bax). Pretreated with TMP significantly reversed this tendency. Several anti-apoptotic characteristics of TMP, including the ability to increase cell viability, inhibit caspase-9 activation, and upregulate Bcl-2 and down-regulate Bax in 4Gy-irradiated lymphocytes were determined. Signal pathway analysis showed TMP could translate nuclear factor-B (NF-B) from cytosol into the nucleus.\Question: Does tetramethylpyrazine protect lymphocytes from radiation-induced apoptosis through nuclear factor-B?
Example Output: The results suggest that TMP had a radioprotective effect through the NF-B pathway to inhibit apoptosis, and it may be an effective candidate for treating radiation diseases associated with cell apoptosis.

Example Input: Context: A computerized physician order entry (CPOE) system with embedded clinical decision support can reduce medication errors in hospitals, but might increase the time taken to generate orders.', 'We aimed to quantify the effects of temporal (month, day of week, hour of shift) and other factors (grade of doctor, prior experience with the system, alert characteristics, and shift type) on the time taken to generate a prescription order.', 'A large university teaching hospital using a locally developed CPOE system with an extensive audit database.', 'We retrospectively analyzed prescription orders from the audit database between August 2011 and July 2012.', 'The geometric mean time taken to generate a prescription order within the CPOE system was 11.75s (95% CI 11.72 to 11.78). Time to prescribe was most affected by the display of high-level (24.59s (24.43 to 24.76); p<0.001) or previously unseen (18.87s (18.78 to 18.96); p<0.001) alerts. Prescribers took significantly less time at weekends (11.29s (11.23 to 11.35)) than on weekdays (11.88s (11.84 to 11.91); p<0.001), in the first (11.25s (11.16 to 11.34); p<0.001) and final (11.56s (11.47 to 11.66); p<0.001) hour of their shifts, and after the first month of using the system.\Question: Do temporal and other factors that influence the time doctors take to prescribe using an electronic prescribing system?
Example Output:
The display of alerts, prescribing experience, system familiarity, and environment all affect the time taken to generate a prescription order. Our study reinforces the need for appropriate alerts to be presented to individuals at an appropriate place in the workflow, in order to improve prescribing efficiency.