Method to Assist in the Scheduling of Add-on Surgical Cases-Upper Prediction Bounds for Surgical Case Durations Based on the Log-normal Distribution 

1998 ◽  
Vol 89 (5) ◽  
pp. 1228-1232 ◽  
Author(s):  
Jinshi Zhou ◽  
Franklin Dexter

Background A problem that operating room (OR) managers face in running an OR suite on the day of surgery is to identify "holes" in the OR schedule in which to assign "add-on" cases. This process necessitates knowing the typical and maximum amounts of time that the case is likely to require. The OR manager may know previous case durations for the particular surgeon performing a particular scheduled procedure. The "upper prediction bound" specifies with a certain probability that the duration of the surgeon's next case will be less than or equal to the bound. Methods Prediction bounds were calculated by using methods that (1) do not assume that case durations follow a specific statistical distribution or (2) assume that case durations follow a log-normal distribution. These bounds were tested using durations of 48,847 cases based on 15,574 combinations of scheduled surgeon and procedure. Results Despite having 3 yr of data, 80 or 90% prediction bounds would not be able to be calculated using the distribution-free method for 35 or 49% of future cases versus 22 or 22% for the log-normal method, respectively. Prediction bounds based on the log-normal distribution overestimated the desired value less often than did the distribution-free method. The chance that the duration of the next case would be less than or equal to its 90% bound based on the log-normal distribution was within 2% of the expected rate. Conclusions Prediction bounds classified by scheduled surgeon and procedure can be accurately calculated using a method that assumes that case durations follow a log-normal distribution.

Biology ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 64
Author(s):  
Arnaud Millet

The mechanosensitivity of cells has recently been identified as a process that could greatly influence a cell’s fate. To understand the interaction between cells and their surrounding extracellular matrix, the characterization of the mechanical properties of natural polymeric gels is needed. Atomic force microscopy (AFM) is one of the leading tools used to characterize mechanically biological tissues. It appears that the elasticity (elastic modulus) values obtained by AFM presents a log-normal distribution. Despite its ubiquity, the log-normal distribution concerning the elastic modulus of biological tissues does not have a clear explanation. In this paper, we propose a physical mechanism based on the weak universality of critical exponents in the percolation process leading to gelation. Following this, we discuss the relevance of this model for mechanical signatures of biological tissues.


2020 ◽  
pp. 150-188
Author(s):  
Richard Holland ◽  
Richard St. John

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