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Particular Variables (particular + variable)

Distribution by Scientific Domains

Medical Sciences	50%

Selected Abstracts

Sugar preferences of nectar feeding birds , a comparison of experimental techniques

JOURNAL OF AVIAN BIOLOGY, Issue 5 2008
Mark Brown
Experiments to determine sugar preferences of nectarivorous animals have been conducted using a wide variety of experimental procedures, all of which aim at ensuring that the solutions offered in choices are "equivalent". Each method used historically has controlled for a particular variable, such as number of molecules in solution, weight of sugar in solution, or amount of energy in solution, depending on what question the researchers have tried to answer. Biologists interpreting these results in terms of bird sugar preference have seldom taken these differences into account. The consequences of using different experimental procedures for sugar preferences exhibited by a nectarivorous bird, the malachite sunbird Nectarinia famosa, were examined using paired sucrose and hexose sugar solutions made up to be either equimolar, equiweight or equicaloric. We found the effect of methodology on bird sugar preference to be quite distinct, especially at low concentrations, where malachite sunbirds showed either sucrose preference, no preference, or hexose preference, depending on the method used. This study highlights the need for researchers to consider methodology when interpreting, or comparing among, results from previous studies. [source]

Risk Factors for Surgical Site Infections in Older People

JOURNAL OF AMERICAN GERIATRICS SOCIETY, Issue 3 2006
Keith S. Kaye MD
OBJECTIVES: To identify risk factors for surgical site infection (SSI) in older people and to test a priori hypotheses regarding particular variables and SSI risk. DESIGN: Case-control study. SETTING: Duke University Medical Center and seven community hospitals in North Carolina and Virginia. PARTICIPANTS: Elderly patients (,65) who underwent surgery between 1991 and 2002 at the study hospitals. Cases were elderly patients with SSI; controls were elderly operative patients without SSI. Infection control practitioners prospectively identified patients. MEASUREMENTS: Data were collected retrospectively. Case patients who developed SSI were compared with control patients who did not develop SSI. RESULTS: Five hundred sixty-nine SSI cases were identified, and 589 uninfected controls were selected. In multivariate analysis, independent predictors of SSI included obesity (odds ratio (OR)=1.77, 95% confidence interval (CI)=1.34,2.32), chronic obstructive pulmonary disease (COPD) (OR=1.66, 95% CI=1.17,2.34), and a wound class classified as contaminated or dirty (OR=1.65, 95% CI=1.01,2.72). Having private insurance was associated with lower risk (OR=0.29, 95% CI=0.12,0.68). CONCLUSION: This study identified several independent predictors of SSI in older people, including comorbid conditions (COPD and obesity), perioperative variables (wound class), and socioeconomic factors (private insurance, which was associated with lower risk). The results from this study can be used to design and implement interventions for SSI prevention in high-risk older people. [source]

Students' perceptions of relationships between some educational variables in the out-patient setting

MEDICAL EDUCATION, Issue 8 2002
D H J M Dolmans
Background Medical education uses the cognitive apprenticeship model of student learning extensively. Students rotate among different hospitals and out- patient clinics where they are exposed to a range of professionally relevant contexts. Here they learn to think and act in different domains under the supervision of experts. Previous research has shown that these learning situations involve little teaching. Students see a narrow range of patient problems and feedback is limited. The aim of this study is to investigate relationships among some educational variables in the out-patient clinic. Method This paper provides a theoretical model that specifies the factors influencing the effectiveness of student rotations at out-patient clinics. The model makes distinctions between input variables, such as organizational quality, number of students contemporaneously involved and available space, and process variables, such as patient mix and supervision, and the output variable of the effectiveness of rotations in out-patient clinics. Results The model was tested against empirical data from evaluative surveys and showed a reasonable fit. The model offers suggestions for improving the learning environment of clinical rotations. Discussion The strength of this study lies in its process evaluation perspective which investigates interactions between intervening variables rather than the influence of particular variables in isolation from other variables. [source]

Imputation and Variable Selection in Linear Regression Models with Missing Covariates

BIOMETRICS, Issue 2 2005
Xiaowei Yang
Summary Across multiply imputed data sets, variable selection methods such as stepwise regression and other criterion-based strategies that include or exclude particular variables typically result in models with different selected predictors, thus presenting a problem for combining the results from separate complete-data analyses. Here, drawing on a Bayesian framework, we propose two alternative strategies to address the problem of choosing among linear regression models when there are missing covariates. One approach, which we call "impute, then select" (ITS) involves initially performing multiple imputation and then applying Bayesian variable selection to the multiply imputed data sets. A second strategy is to conduct Bayesian variable selection and missing data imputation simultaneously within one Gibbs sampling process, which we call "simultaneously impute and select" (SIAS). The methods are implemented and evaluated using the Bayesian procedure known as stochastic search variable selection for multivariate normal data sets, but both strategies offer general frameworks within which different Bayesian variable selection algorithms could be used for other types of data sets. A study of mental health services utilization among children in foster care programs is used to illustrate the techniques. Simulation studies show that both ITS and SIAS outperform complete-case analysis with stepwise variable selection and that SIAS slightly outperforms ITS. [source]