Quality Tolerance Limits: A General Guidance for Parameter Selection and Threshold Setting.

The past years have sharpened the industry's understanding of a Quality by Design (QbD) approach toward clinical trials. Using QbD encourages designing quality into a trial during the planning phase. The identification of Critical to Quality (CtQs) factors and specifically Critical Data and Processes (CD&Ps) is key to such a risk-based monitoring approach. A variable that allows monitoring the evolution of risk regarding the CD&Ps is called a Quality Tolerance Limit (QTL) parameter. These parameters are linked to the scientific question(s) of a trial and may identify the issues that can jeopardize the integrity of trial endpoints. This paper focuses on defining what QTL parameters are and providing general guidance on setting thresholds for these parameters allowing for the derivation of an acceptable range of the risk.

Degradation-Sensitive Health Indicator Construction for Precise Insulation Degradation Monitoring of Electromagnetic Coils.

Electromagnetic coils are indispensable components for energy conversion and transformation in various systems across industries. However, electromagnetic coil insulation failure occurs frequently, which can lead to serious consequences. To facilitate predictive maintenance for industrial systems, it is essential to monitor insulation degradation prior to the formation of turn-to-turn shorts. This paper experimentally investigates coil insulation degradation from both macro and micro perspectives. At the macro level, an evaluation index based on a weighted linear combination of trend, monotonicity and robustness is proposed to construct a degradation-sensitive health indicator (DSHI) based on high-frequency electrical response parameters for precise insulation degradation monitoring. While at the micro level, a coil finite element analysis and twisted pair accelerated degradation test are conducted to obtain the actual turn-to-turn insulation status. The correlation analysis between macroscopic and microscopic effects of insulation degradation is used to verify the proposed DSHI-based method. Further, it helps to determine the threshold of DSHI. This breakthrough opens new possibilities for predictive maintenance for industrial equipment that incorporates coils.

Event-Triggered Kalman Filter and Its Performance Analysis.

In estimation of linear systems, an efficient event-triggered Kalman filter algorithm is proposed. Based on the hypothesis test of Gaussian distribution, the significance of the event-triggered threshold is given. Based on the threshold, the actual trigger frequency of the estimated system can be accurately set. Combining the threshold and the proposed event-triggered mechanism, an event-triggered Kalman filter is proposed and the approximate estimation accuracy can also be calculated. Whether it is a steady system or a time-varying system, the proposed algorithm can reasonably set the threshold according to the required accuracy in advance. The proposed event-triggered estimator not only effectively reduces the communication cost, but also has high accuracy. Finally, simulation examples verify the correctness and effectiveness of the proposed algorithm.

AN EXAMINATION OF THRESHOLD SETTING IN SOCIAL EMOTIONAL MEASUREMENT.

Background and Purpose: Thresholds are used by nurses in social emotional screening. A purpose of this study is to examine fluctuation of the threshold pattern in a widely-used instrument, Ages and Stages Questionnaire-Social Emotional, Second Edition (ASQ:SE-2). Methods: Threshold settings are investigated against an irreversible pattern of child growth from research literature. Empirical studies are conducted on an issue of 6,039 missing cases in the ASQ:SE-2 normative sample that undermines data quality for the cutoff score configuration. Results: A Bayesian estimate has been suggested to improve the threshold at age 3 with an asymptotically unbiased cutoff score. Given the coverage of ASQ:SE-2 for children from less than 6 months to 72 months for mental health referrals, this study is particularly relevant to the practice of social emotional screening performed by pediatric nurses, school nurses, and nurse practitioners at mental health hospitals. Conclusion: To address the violation of Classical Test Theory and Item Response Theory in the ASQ:SE-2 threshold settings, consistent statistical imputations are needed to maintain monotonicity of the cutoff score patterns that are aligned with the irreversible trend of child growth, as well as the well-established national standards for missing data examination.

Stream-data-clustering based adaptive alarm threshold setting approaches for industrial processes with multiple operating conditions.

The setting of alarm thresholds is a critical concern of alarm management systems in industrial processes. Conventional alarm thresholds less consider changes of operating conditions in production processes, which degrades the effectiveness of alarm management systems. In response to this problem, this paper proposes an adaptive alarm threshold setting approach based on stream data clustering (SDC). Firstly, we develop a stream data clustering algorithm termed as a-DenStream algorithm which realizes industrial flow data clustering through online micro-clustering and offline integration. Subsequently, we develop the C-BOUND algorithm to extract the edges of the clustering results. In response to alarms associated with multiple operating conditions, segmentations are conducted to set alarm threshold groups and build a multi-condition alarm threshold model. Consequently, an adaptive alarm threshold setting method based on model matching is created. The effectiveness of the proposed method is demonstrated by experiments on a coal gasification chemical process. The proposed method provides a potential application for industrial processes with multiple operating conditions alarm managements.

Do We Really Need More Stages? Comparing the Effects of Likelihood Alarm Systems and Binary Alarm Systems.

OBJECTIVE: This research investigates the potential behavioral and performance benefits of a four-stage likelihood alarm system (4-LAS) contrasting a 3-LAS, a binary alarm system with a liberal threshold (lib-BAS), and a BAS with a conservative threshold (con-BAS). BACKGROUND: Prior research has shown performance benefits of 3-LASs over conventional lib-BASs due to more distinct response strategies and better discriminating true from false alerts. This effect might be further enhanced using 4-LASs. However, the increase in stages could cause users to reduce cognitive complexity by responding in the same way to the two lower and the two higher stages, thus treating the 4-LAS like a con-BAS. METHOD: All systems were compared using a dual-task paradigm. Response strategies, number of joint human machine (JHM) false alarms (FAs), misses, and sensitivity were regarded. RESULTS: Compared with the lib-BAS, JHM sensitivity only improved with the 4-LAS and the con-BAS. However, the number of JHM misses was lowest for the con-BAS compared with all other systems. CONCLUSION: JHM sensitivity improvements can be achieved by using a 4-LAS, as well as a con-BAS. However, only the latter one may also reduce the number of JHM misses, which is remarkable considering that BASs with conservative thresholds a priori commit more inbuilt misses than other systems. APPLICATION: Results suggest implementing conservative BASs in multi-task working environments to improve JHM sensitivity and reduce the number of JHM misses. When refraining from designing systems which are miss prone, 4-LASs represent a suitable compromise.

A quantitative evaluation on the image-quality parameters and quality assurance thresholds setting of accelerator on-board imaging system / 中华放射肿瘤学杂志

Objective@#To achieve quantitative analysis of image quality parameters and establish warning and action thresholds for the on-board imaging (OBI) system of linear accelerator.@*Methods@#The Catphan604 phantom was repeatedly scanned in the Full-Fan and Half-Fan CBCT scanning modes on a Varian EDGE linear accelerator, and the software based on Python language development in-house was utilized to analyze image quality parameters, such as CT number linearity, geometric consistency, slice thickness, spatial resolution, uniformity and low-contrast resolution. The quantitative analysis results of each image quality parameter obtained from 16 times of scanning within 16 months were normalized to the mean and the standard deviations were recorded. A run chart analysis was created to determine the warnings and action thresholds.@*Results@#The software built in-house can quantitatively analyze the image parameters of the two scanning modes of OBI system. The low-contrast resolution of Half-Fan was better than that of Full-Fan, whereas the spatial resolution of Full-Fan was superior to that of Half-Fan. One standard deviation (1σ) was set as the warning threshold and 2 standard deviations (2σ) as the action threshold, respectively. The tolerance level of Half-Fan was smaller than that of Full-Fan.@*Conclusion@#Self-developed software enables quantitative analysis of accelerator image quality parameters, establishes warning and action tolerance of quality assurance and provides guidance for image quality assurance under image-guided radiotherapy specification.

The threshold setting of gastric residual volume of ICU patients :a literature review / 肠外与肠内营养

Clinicians monitor the gastric residual volume(GRV) of critical ill patients to minimize or prevent side effect of EN.Nevertheless,recommendations about the threshold GRV in critically ill patients treated with EN are not uniform.Many recent clinical studies had demonstrated that increasing the limit of GRV was associated with the increase in the diet volume ratio of ICU patients receiving EN,and it was not associated with adverse effects in gastrointestinal complications.This review summarizes the influence factors of GRV monitoring to evaluate the feasibility and necessity of increasing the threshold of GRV.

A complementary graphical method for reducing and analyzing large data sets. Case studies demonstrating thresholds setting and selection.

OBJECTIVES: Graphical displays can make data more understandable; however, large graphs can challenge human comprehension. We have previously described a filtering method to provide high-level summary views of large data sets. In this paper we demonstrate our method for setting and selecting thresholds to limit graph size while retaining important information by applying it to large single and paired data sets, taken from patient and bibliographic databases. METHODS: Four case studies are used to illustrate our method. The data are either patient discharge diagnoses (coded using the International Classification of Diseases, Clinical Modifications [ICD9-CM]) or Medline citations (coded using the Medical Subject Headings [MeSH]). We use combinations of different thresholds to obtain filtered graphs for detailed analysis. The thresholds setting and selection, such as thresholds for node counts, class counts, ratio values, p values (for diff data sets), and percentiles of selected class count thresholds, are demonstrated with details in case studies. The main steps include: data preparation, data manipulation, computation, and threshold selection and visualization. We also describe the data models for different types of thresholds and the considerations for thresholds selection. RESULTS: The filtered graphs are 1%-3% of the size of the original graphs. For our case studies, the graphs provide 1) the most heavily used ICD9-CM codes, 2) the codes with most patients in a research hospital in 2011, 3) a profile of publications on "heavily represented topics" in MEDLINE in 2011, and 4) validated knowledge about adverse effects of the medication of rosiglitazone and new interesting areas in the ICD9-CM hierarchy associated with patients taking the medication of pioglitazone. CONCLUSIONS: Our filtering method reduces large graphs to a manageable size by removing relatively unimportant nodes. The graphical method provides summary views based on computation of usage frequency and semantic context of hierarchical terminology. The method is applicable to large data sets (such as a hundred thousand records or more) and can be used to generate new hypotheses from data sets coded with hierarchical terminologies.