Practical sources of error in measuring pulmonary artery occlusion pressure: a study in participants of a special intensivist training program of The Scandinavian Society of Anaesthesiology and Intensive Care Medicine (SSAI)

BACKGROUND: Physiological data obtained with the pulmonary artery catheter (PAC) are susceptible to errors in measurement and interpretation. Little attention has been paid to the relevance of errors in hemodynamic measurements performed in the intensive care unit (ICU). The aim of this study was to assess the errors related to the technical aspects (zeroing and reference level) and actual measurement (curve interpretation) of the pulmonary artery occlusion pressure (PAOP). METHODS: Forty-seven participants in a special ICU training program and 22 ICU nurses were tested without pre-announcement. All participants had previously been exposed to the clinical use of the method. The first task was to set up a pressure measurement system for PAC (zeroing and reference level) and the second to measure the PAOP. RESULTS: The median difference from the reference mid-axillary zero level was - 3 cm (-8 to + 9 cm) for physicians and -1 cm (-5 to + 1 cm) for nurses. The median difference from the reference PAOP was 0 mmHg (-3 to 5 mmHg) for physicians and 1 mmHg (-1 to 15 mmHg) for nurses. When PAOP values were adjusted for the differences from the reference transducer level, the median differences from the reference PAOP values were 2 mmHg (-6 to 9 mmHg) for physicians and 2 mmHg (-6 to 16 mmHg) for nurses. CONCLUSIONS: Measurement of the PAOP is susceptible to substantial error as a result of practical mistakes. Comparison of results between ICUs or practitioners is therefore not possible.

Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between “a priori” methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and “a posteriori” methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state-of-the-art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real-world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.

Practical domain-specific debuggers using the Moldable Debugger framework

Understanding the run-time behavior of software systems can be a challenging activity. Debuggers are an essential category of tools used for this purpose as they give developers direct access to the running systems. Nevertheless, traditional debuggers rely on generic mechanisms to introspect and interact with the running systems, while developers reason about and formulate domain-specific questions using concepts and abstractions from their application domains. This mismatch creates an abstraction gap between the debugging needs and the debugging support leading to an inefficient and error-prone debugging effort, as developers need to recover concrete domain concepts using generic mechanisms. To reduce this gap, and increase the efficiency of the debugging process, we propose a framework for developing domain-specific debuggers, called the Moldable Debugger, that enables debugging at the level of the application domain. The Moldable Debugger is adapted to a domain by creating and combining domain-specific debugging operations with domain-specific debugging views, and adapts itself to a domain by selecting, at run time, appropriate debugging operations and views. To ensure the proposed model has practical applicability (i.e., can be used in practice to build real debuggers), we discuss, from both a performance and usability point of view, three implementation strategies. We further motivate the need for domain-specific debugging, identify a set of key requirements and show how our approach improves debugging by adapting the debugger to several domains.