The inter-rater reliability of the diagnosis of surgical site infection in the context of a clinical trial.

ObjectivesThe diagnosis of surgical site infection following endoprosthetic reconstruction for bone tumours is frequently a subjective diagnosis. Large clinical trials use blinded Central Adjudication Committees (CACs) to minimise the variability and bias associated with assessing a clinical outcome. The aim of this study was to determine the level of inter-rater and intra-rater agreement in the diagnosis of surgical site infection in the context of a clinical trial.Materials and methodsThe Prophylactic Antibiotic Regimens in Tumour Surgery (PARITY) trial CAC adjudicated 29 non-PARITY cases of lower extremity endoprosthetic reconstruction. The CAC members classified each case according to the Centers for Disease Control (CDC) criteria for surgical site infection (superficial, deep, or organ space). Combinatorial analysis was used to calculate the smallest CAC panel size required to maximise agreement. A final meeting was held to establish a consensus.ResultsFull or near consensus was reached in 20 of the 29 cases. The Fleiss kappa value was calculated as 0.44 (95% confidence interval (CI) 0.35 to 0.53), or moderate agreement. The greatest statistical agreement was observed in the outcome of no infection, 0.61 (95% CI 0.49 to 0.72, substantial agreement). Panelists reached a full consensus in 12 of 29 cases and near consensus in five of 29 cases when CDC criteria were used (superficial, deep or organ space). A stable maximum Fleiss kappa of 0.46 (95% CI 0.50 to 0.35) at CAC sizes greater than three members was obtained.ConclusionsThere is substantial agreement among the members of the PARITY CAC regarding the presence or absence of surgical site infection. Agreement on the level of infection, however, is more challenging. Additional clinical information routinely collected by the prospective PARITY trial may improve the discriminatory capacity of the CAC in the parent study for the diagnosis of infection.Cite this article: J. Nuttall, N. Evaniew, P. Thornley, A. Griffin, B. Deheshi, T. O'Shea, J. Wunder, P. Ferguson, R. L. Randall, R. Turcotte, P. Schneider, P. McKay, M. Bhandari, M. Ghert. The inter-rater reliability of the diagnosis of surgical site infection in the context of a clinical trial. Bone Joint Res 2016;5:347-352. DOI: 10.1302/2046-3758.58.BJR-2016-0036.R1

Algorithmic and Statistical Perspectives on Large-Scale Data Analysis

In recent years, ideas from statistics and scientific computing have begun to interact in increasingly sophisticated and fruitful ways with ideas from computer science and the theory of algorithms to aid in the development of improved worst-case algorithms that are useful for large-scale scientific and Internet data analysis problems. In this chapter, I will describe two recent examples---one having to do with selecting good columns or features from a (DNA Single Nucleotide Polymorphism) data matrix, and the other having to do with selecting good clusters or communities from a data graph (representing a social or information network)---that drew on ideas from both areas and that may serve as a model for exploiting complementary algorithmic and statistical perspectives in order to solve applied large-scale data analysis problems.Comment: 33 pages. To appear in Uwe Naumann and Olaf Schenk, editors, "Combinatorial Scientific Computing," Chapman and Hall/CRC Press, 201

Surrogate modeling based cognitive decision engine for optimization of WLAN performance

Due to the rapid growth of wireless networks and the dearth of the electromagnetic spectrum, more interference is imposed to the wireless terminals which constrains their performance. In order to mitigate such performance degradation, this paper proposes a novel experimentally verified surrogate model based cognitive decision engine which aims at performance optimization of IEEE 802.11 links. The surrogate model takes the current state and configuration of the network as input and makes a prediction of the QoS parameter that would assist the decision engine to steer the network towards the optimal configuration. The decision engine was applied in two realistic interference scenarios where in both cases, utilization of the cognitive decision engine significantly outperformed the case where the decision engine was not deployed

Synthesis of all-digital delay lines

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe synthesis of delay lines (DLs) is a core task during the generation of matched delays, ring oscillator clocks or delay monitors. The main figure of merit of a DL is the fidelity to track variability. Unfortunately, complex systems have a great diversity of timing paths that exhibit different sensitivities to static and dynamic variations. Designing DLs that capture this diversity is an ardous task. This paper proposes an algorithmic approach for the synthesis of DLs that can be integrated in a conventional design flow. The algorithm uses heuristics to perform a combinatorial search in a vast space of solutions that combine different types of gates and wire lengths. The synthesized DLs are (1) all digital, i.e., built of conventional standard cells, (2) accurate in tracking variability and (3) configurable at runtime. Experimental results with a commercial standard cell library confirm the quality of the DLs that only exhibit delay mismatches of about 1% on average over all PVT corners.Peer ReviewedPostprint (author's final draft

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making