York University

About: York University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Population & Politics. The organization has 18899 authors who have published 43357 publications receiving 1568560 citations.

Beyond Dualism: Stability and Change As a Duality

Abstract: To survive and prosper, organizations must reconcile stability, reliability, and exploitation with change, innovation, and exploration. These imperatives and the mechanisms that support them are generally seen as incompatible and mutually exclusive. I present an alternative: a duality view in which stability and change are fundamentally interdependent—contradictory but also mutually enabling. This view revisits several enduring ideas about stability and change and offers theoretical and pragmatic opportunities to dissolve and transcend their paradoxical relationship.

Optimal database combinations for literature searches in systematic reviews: a prospective exploratory study.

Abstract: Within systematic reviews, when searching for relevant references, it is advisable to use multiple databases. However, searching databases is laborious and time-consuming, as syntax of search strategies are database specific. We aimed to determine the optimal combination of databases needed to conduct efficient searches in systematic reviews and whether the current practice in published reviews is appropriate. While previous studies determined the coverage of databases, we analyzed the actual retrieval from the original searches for systematic reviews. Since May 2013, the first author prospectively recorded results from systematic review searches that he performed at his institution. PubMed was used to identify systematic reviews published using our search strategy results. For each published systematic review, we extracted the references of the included studies. Using the prospectively recorded results and the studies included in the publications, we calculated recall, precision, and number needed to read for single databases and databases in combination. We assessed the frequency at which databases and combinations would achieve varying levels of recall (i.e., 95%). For a sample of 200 recently published systematic reviews, we calculated how many had used enough databases to ensure 95% recall. A total of 58 published systematic reviews were included, totaling 1746 relevant references identified by our database searches, while 84 included references had been retrieved by other search methods. Sixteen percent of the included references (291 articles) were only found in a single database; Embase produced the most unique references (n = 132). The combination of Embase, MEDLINE, Web of Science Core Collection, and Google Scholar performed best, achieving an overall recall of 98.3 and 100% recall in 72% of systematic reviews. We estimate that 60% of published systematic reviews do not retrieve 95% of all available relevant references as many fail to search important databases. Other specialized databases, such as CINAHL or PsycINFO, add unique references to some reviews where the topic of the review is related to the focus of the database. Optimal searches in systematic reviews should search at least Embase, MEDLINE, Web of Science, and Google Scholar as a minimum requirement to guarantee adequate and efficient coverage.

Skyline with presorting

Abstract: The skyline, or Pareto, operator selects those tuples that are not dominated by any others. Extending relational systems with the skyline operator would offer a basis for handling preference queries. Good algorithms are needed for skyline, however, to make this efficient in a relational setting. We propose a skyline algorithm, SFS, based on presorting that is general, for use with any skyline query, efficient, and well behaved in a relational setting.

Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods.

Abstract: ContextComparisons of outcomes between patients treated and untreated in observational studies may be biased due to differences in patient prognosis between groups, often because of unobserved treatment selection biases.ObjectiveTo compare 4 analytic methods for removing the effects of selection bias in observational studies: multivariable model risk adjustment, propensity score risk adjustment, propensity-based matching, and instrumental variable analysis.Design, Setting, and PatientsA national cohort of 122 124 patients who were elderly (aged 65-84 years), receiving Medicare, and hospitalized with acute myocardial infarction (AMI) in 1994-1995, and who were eligible for cardiac catheterization. Baseline chart reviews were taken from the Cooperative Cardiovascular Project and linked to Medicare health administrative data to provide a rich set of prognostic variables. Patients were followed up for 7 years through December 31, 2001, to assess the association between long-term survival and cardiac catheterization within 30 days of hospital admission.Main Outcome MeasureRisk-adjusted relative mortality rate using each of the analytic methods.ResultsPatients who received cardiac catheterization (n = 73 238) were younger and had lower AMI severity than those who did not. After adjustment for prognostic factors by using standard statistical risk-adjustment methods, cardiac catheterization was associated with a 50% relative decrease in mortality (for multivariable model risk adjustment: adjusted relative risk [RR], 0.51; 95% confidence interval [CI], 0.50-0.52; for propensity score risk adjustment: adjusted RR, 0.54; 95% CI, 0.53-0.55; and for propensity-based matching: adjusted RR, 0.54; 95% CI, 0.52-0.56). Using regional catheterization rate as an instrument, instrumental variable analysis showed a 16% relative decrease in mortality (adjusted RR, 0.84; 95% CI, 0.79-0.90). The survival benefits of routine invasive care from randomized clinical trials are between 8% and 21%.ConclusionsEstimates of the observational association of cardiac catheterization with long-term AMI mortality are highly sensitive to analytic method. All standard risk-adjustment methods have the same limitations regarding removal of unmeasured treatment selection biases. Compared with standard modeling, instrumental variable analysis may produce less biased estimates of treatment effects, but is more suited to answering policy questions than specific clinical questions.