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.

Q: Context: Patient-prosthesis mismatch has been identified as a risk factor for mortality after aortic valve replacement and for structural valve deterioration (SVD) in patients receiving a bioprosthetic aortic valve. The aim of the present study was to compare the incidence of aortic valve bioprosthesis replacement for SVD in patients with mismatch to a population without mismatch.', 'Three hundred eighty-seven adult patients who underwent aortic valve replacement with a bioprosthesis from 1974 to 2009 were retrospectively reviewed. Mismatch was considered to be present if the anticipated indexed effective orifice area was <0.70cm(2) /m(2) . The median follow-up period was 7.2 years. Follow-up was 97% complete.', "Patient-prosthesis mismatch was present in 12% of the study population (n=47). Ten-year freedom from reoperation for aortic bioprosthesis replacement was 74.33.2%. During follow-up, 111 patients underwent reoperation for aortic bioprosthesis replacement. Causes of aortic bioprosthesis replacement were SVD of the bioprosthesis (n=96), paravalvular leak (n=10), and acute endocarditis (n=5). According to unadjusted Kaplan-Meier analysis, patients with mismatch had a higher incidence of aortic bioprosthesis replacement for SVD when compared with patients without mismatch (log rank test: p 0.05). This result was confirmed by multivariable Cox regression analysis, which identified two independent predictors of aortic bioprosthesis replacement for SVD: patients' age (hazard ratio (HR) 0.967) and patient-prosthesis mismatch (HR 2.161).\Question: Does patient-prosthesis mismatch in patients undergoing bioprosthetic aortic valve implantation increase risk of reoperation for structural valve deterioration?

A: Patients suffering from mismatch were twice as likely to undergo reoperation for aortic bioprosthesis replacement for SVD than those without mismatch.
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Q: Context: Information about malaria risk factors at high altitudes is scanty. Understanding the risk factors that determine the risk of malaria transmission at high altitude villages is important to facilitate implementing sustainable malaria control and prevention programs.', 'An unmatched case control study was conducted among patients seeking treatment at health centers in high altitude areas. Either microscopy or rapid diagnostic tests were used to confirm the presence of plasmodium species. A generalized linear model was used to identify the predictors of malaria transmission in high altitude villages.', 'Males (AOR=3.11, 95%CI: 2.28, 4.23), and those who traveled away from the home in the previous month (AOR=2.01, 95% CI: 1.56, 2.58) were strongly associated with presence of malaria in high altitude villages. Other significant factors, including agriculture in occupation (AOR=1.41, 95% CI: 1.05, 1.93), plants used for fencing (AOR=1.70, 95% CI: 1.18, 2.52) and forests near the house (AOR=1.60, 95% CI: 1.15, 2.47), were found predictors for malaria in high altitude villages.\Question: Are men traveling away from home more likely to bring malaria into high altitude villages , northwest Ethiopia?

A: Travel outside of their home was an important risk of malaria infections acquisition. Targeting males who frequently travel to malarious areas can reduce malaria transmission risks in high altitude areas.
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Q: Context: Manual scoring of sleep relies on identifying certain characteristics in polysomnograph (PSG) signals. However, these characteristics are disrupted in patients with neurodegenerative diseases.', "This study evaluates sleep using a topic modeling and unsupervised learning approach to identify sleep topics directly from electroencephalography (EEG) and electrooculography (EOG). PSG data from control subjects were used to develop an EOG and an EEG topic model. The models were applied to PSG data from 23 control subjects, 25 patients with periodic leg movements (PLMs), 31 patients with idiopathic REM sleep behavior disorder (iRBD) and 36 patients with Parkinson's disease (PD). The data were divided into training and validation datasets and features reflecting EEG and EOG characteristics based on topics were computed. The most discriminative feature subset for separating iRBD/PD and PLM/controls was estimated using a Lasso-regularized regression model.", 'The features with highest discriminability were the number and stability of EEG topics linked to REM and N3, respectively. Validation of the model indicated a sensitivity of 91.4% and a specificity of 68.8% when classifying iRBD/PD patients.\Question: Does data-driven modeling of sleep EEG and EOG reveal characteristics indicative of pre-Parkinson 's and Parkinson 's disease?

A:
The topics showed visual accordance with the manually scored sleep stages, and the features revealed sleep characteristics containing information indicative of neurodegeneration.
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