JEMS Journal of Emergency Medical Services

Published by Elsevier
Print ISSN: 0197-2510
Publications
A qualitative calculus based on signs was first developed by economists to overcome one major difficulty: we often face a lack of quantitative data. A number of methods such as comparative statics were proposed to solve qualitative-model-based problems. A new interest has recently arisen in qualitative methods as applied to process control, control theory, and artificial intelligence (the author is himself involved in Qualitative Physics - a subfield of AI). New approaches, such as order of magnitude reasoning, have extended the scope of "what is qualitative". Beyond some aspects specific to these fields - for instance representing how humans reason on the behavior of a system is a major concern in AI - this has led to new results and methods for dealing with qualitative models. These results are likely to be applicable within any framework, including economical modelling. This paper discusses these new theoretical aspects of qualitative calculus. We hope that it will provide a better insight into these mathematical-like topics and contribute to the emergence of a general theory as to "what is qualitative".
 
The size of the study should be considered early in the planning phase. In some instances, no formal sample size is ever calculated. Instead, the number of participants available to the investigators during some period of time determines the size of the study. Many clinical trials that do not carefully consider the sample size requirements turn out to lack the statistical power or ability to detect intervention effects of a magnitude that has clinical importance. In 1978, Freiman and colleagues [1] reviewed the power of 71 published randomized controlled clinical trials, which failed to find significant differences between groups. “Sixty-seven of the trials had a greater than 10% risk of missing a true 25% therapeutic improvement, and with the same risk, 50 of the trials could have missed a 50% improvement.” In other instances, the sample size estimation may assume an unrealistically large intervention effect. Thus, the power for more realistic intervention effects will be low or less than desired. The danger in studies with low statistical power is that interventions that could be beneficial are discarded without adequate testing and may never be considered again. Certainly, many studies do contain appropriate sample size estimates, but many are still too small.
 
Hypertension is a disease process commonly seen in patients who seek emergency medical care. A prolonged increase in BP or a sudden acute rise in pressure can cause a plethora of problems. Most commonly the cardiovascular and cerebrovascular systems are affected. Direct EMS care at stabilizing the ABCs and preventing further end-organ damage.
 
Top-cited authors
Jeffrey T. Mitchell
  • University of Maryland, Baltimore County
Jeffrey P Salomone
Marlena M Wald
Ernest Sullivent
  • United States Coast Guard Academy
Robert L Galli
  • University of Mississippi Medical Center