Related Experiment Video
Updated: Mar 13, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Quantification of hazard prediction ability at hazard prediction training (Kiken-Yochi Training: KYT) by
Masahiro Hashida1,2, Ryousuke Kamezaki3, Makoto Goto3
1Department of Radiology, Hospital Division of Medical Technology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-ku, Kumamoto City, Kumamoto, 860-8556, Japan. mhashida@fc.kuh.kumamoto-u.ac.jp.
Abstract:
The ability to predict hazards in possible situations in a general X-ray examination room created for Kiken-Yochi training (KYT) is quantified by use of free-response receiver-operating characteristics (FROC) analysis for determining whether the total number of years of clinical experience, involvement in general X-ray examinations, occupation, and training each have an impact on the hazard prediction ability. Twenty-three radiological technologists (RTs) (years of experience: 2-28), four nurses (years of experience: 15-19), and six RT students observed 53 scenes of KYT: 26 scenes with hazardous points (hazardous points are those that might cause injury to patients) and 27 scenes without points. Based on the results of these observations, we calculated the alternative free-response receiver-operating characteristic (AFROC) curve and the figure of merit (FOM) to quantify the hazard prediction ability. The results showed that the total number of years of clinical experience did not have any impact on hazard prediction ability, whereas recent experience with general X-ray examinations greatly influenced this ability. In addition, the hazard prediction ability varied depending on the occupations of the observers while they were observing the same scenes in KYT. The hazard prediction ability of the radiologic technology students was improved after they had undergone patient safety training. This proposed method with FROC observer study enabled the quantification and evaluation of the hazard prediction capability, and the application of this approach to clinical practice may help to ensure the safety of examinations and treatment in the radiology department.
Related Concept Videos
Hazard Rate
Hazard Ratio
For example, in a clinical trial...
Survival Tree
Building a Survival Tree
Constructing a...
Receiver Operating Characteristic Plot
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Design Example: Analyzing Capacity Contours for Flood Risk Assessment
