IDEMPOTENT UNINORMS ON FINITE ORDINAL SCALES

2009 ◽  
Vol 17 (01) ◽  
pp. 1-14 ◽  
Author(s):  
BERNARD DE BAETS ◽  
JÁNOS FODOR ◽  
DANIEL RUIZ-AGUILERA ◽  
JOAN TORRENS
Keyword(s):  
Ordinal Scale ◽  
Neutral Element ◽  
Ordinal Scales ◽  
One To One

In this paper we characterize all idempotent uninorms defined on a finite ordinal scale. It is proved that any such discrete idempotent uninorm is uniquely determined by a decreasing function from the set of scale elements not greater than the neutral element to the set of scale elements not smaller than the neutral element, and vice versa. Based on this one-to-one correspondence, the total number of discrete idempotent uninorms on a finite ordinal scale of n + 1 elements is equal to 2n.

scholarly journals Risks of using pre-event time scale and ordinal scale measurements in COVID-19 clinical trials

2021 ◽  
pp. 32-37
Author(s):  
M.V. Yashchenko
Keyword(s):  
Clinical Trials ◽  
Clinical Outcome ◽  
Oxygen Therapy ◽  
Resource Dependence ◽  
Hospital Admissions ◽  
Outcome Data ◽  
Phase Iii ◽  
Ordinal Scale ◽  
Time To Event ◽  
Ordinal Scales

BACKGROUND. The article shows the results of literature search and analysis of endpoints of interventional clinical trials of phase III-IV of the treatment of hospitalized patients with coronavirus disease (COVID-19) and of its prevention. MATERIALS AND METHODS. Among 102 trials found, ordinal scales were used in 60 trials, time-to-event outcome measures were used in 54 trials, both scales – in 49 trials. Time-to-event endpoints were related to hospitalization/intensive care unit term, discontinuation of oxygen therapy, and clinical improvement standardized on ordinal scales. At the same time, the early discontinuation of oxygen therapy and the early discharge create risks to the biometric measurement. RESULTS AND DISCUSSION. Statistical calculations showed the association of the number of new COVID-19 hospital admissions per day with the percentage of free beds, but not only with the number of new coronavirus infection cases in general, the number of deaths and the number of people recovering from COVID-19 per day in different regions of Ukraine. These results may indicate that resource-dependence and organizational aspects affect the hospitalization of patients with COVID-19. CONCLUSIONS. Therefore, to ensure that the discharge or discontinuation of oxygen therapy was due solely to a positive clinical outcome, data on changes of number of beds, access to oxygen supplies as well as data relevant to determination of the desired clinical outcome (body temperature, oxygen saturation, severity of symptoms, etc.) should be collected. It is recommended to collect biomarker data after discharge, if possible.

scholarly journals Understanding the ramifications of quantitative ordinal scales on accuracy of estimates of disease severity and data analysis in plant pathology

2021 ◽  
Author(s):  
Kuo-Szu Chiang ◽  
Clive H. Bock
Keyword(s):  
Plant Pathology ◽  
Disease Severity ◽  
Statistical Power ◽  
Plant Diseases ◽  
Ordinal Scale ◽  
Disease Assessment ◽  
Ordinal Scales ◽  
Scale Design ◽  
Data Design ◽  
Scale Data

AbstractThe severity of plant diseases, traditionally defined as the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases but is prone to error. Plant pathologists face many situations in which the measurement by nearest percent estimates (NPEs) of disease severity is time-consuming or impractical. Moreover, rater NPEs of disease severity are notoriously variable. Therefore, NPEs of disease may be of questionable value if severity cannot be determined accurately and reliably. In such situations, researchers have often used a quantitative ordinal scale of measurement—often alleging the time saved, and the ease with which the scale can be learned. Because quantitative ordinal disease scales lack the resolution of the 0 to 100% scale, they are inherently less accurate. We contend that scale design and structure have ramifications for the resulting analysis of data from the ordinal scale data. To minimize inaccuracy and ensure that there is equivalent statistical power when using quantitative ordinal scale data, design of the scales can be optimized for use in the discipline of plant pathology. In this review, we focus on the nature of quantitative ordinal scales used in plant disease assessment. Subsequently, their application and effects will be discussed. Finally, we will review how to optimize quantitative ordinal scales design to allow sufficient accuracy of estimation while maximizing power for hypothesis testing.

scholarly journals Quantitative Ordinal Scale Estimates of Plant Disease Severity: Comparing Treatments Using a Proportional Odds Model

2020 ◽  
Vol 110 (4) ◽  
pp. 734-743 ◽  
Author(s):  
K. S. Chiang ◽  
H. I. Liu ◽  
Y. L. Chen ◽  
M. El Jarroudi ◽  
C. H. Bock
Keyword(s):  
Disease Severity ◽  
Hypothesis Test ◽  
Standard Procedure ◽  
Simulation Method ◽  
Superior Performance ◽  
Ordinal Scale ◽  
Proportional Odds ◽  
Ordinal Scales ◽  
Proportional Odds Models ◽  
Percent Area

Studies in plant pathology, agronomy, and plant breeding requiring disease severity assessment often use quantitative ordinal scales (i.e., a special type of ordinal scale that uses defined numeric ranges); a frequently used example of such a scale is the Horsfall-Barratt scale. Parametric proportional odds models (POMs) may be used to analyze the ratings obtained from quantitative ordinal scales directly, without converting ratings to percent area affected using range midpoints of such scales (currently a standard procedure). Our aim was to evaluate the performance of the POM for comparing treatments using ordinal estimates of disease severity relative to two alternatives, the midpoint conversions (MCs) and nearest percent estimates (NPEs). A simulation method was implemented and the parameters of the simulation estimated using actual disease severity data from the field. The criterion for comparison of the three approaches was the power of the hypothesis test (the probability to reject the null hypothesis when it is false). Most often, NPEs had superior performance. The performance of the POM was never inferior to using the MC at severity <40%. Especially at low disease severity (≤10%), the POM was superior to using the MC method. Thus, for early onset of disease or for comparing treatments with severities <40%, the POM is preferable for analyzing disease severity data based on quantitative ordinal scales when comparing treatments and at severities >40% is equivalent to other methods.

A Support Program

1994 ◽  
Vol 25 (2) ◽  
pp. 112-114 ◽  
Author(s):  
Henna Grunblatt ◽  
Lisa Daar
Keyword(s):  
Hearing Aids ◽  
School Counselor ◽  
Support Program ◽  
Educational Audiology ◽  
Part Program ◽  
One To One

A program for providing information to children who are deaf about their deafness and addressing common concerns about deafness is detailed. Developed by a school audiologist and the school counselor, this two-part program is geared for children from 3 years to 15 years of age. The first part is an educational audiology program consisting of varied informational classes conducted by the audiologist. Five topics are addressed in this part of the program, including basic audiology, hearing aids, FM systems, audiograms, and student concerns. The second part of the program consists of individualized counseling. This involves both one-to-one counseling sessions between a student and the school counselor, as well as conjoint sessions conducted—with the student’s permission—by both the audiologist and the school counselor.

Quick Consult: Quick Consult: Acromioplasty

2002 ◽  
Vol 7 (3) ◽  
pp. 4-5
Keyword(s):  
Glasgow Coma Scale ◽  
Outcome Assessment ◽  
High Probability ◽  
Vegetative State ◽  
Behavioral Impairment ◽  
Whole Person ◽  
Ordinal Scales ◽  
Probability Of Death ◽  
The Individual ◽  
Assessment Scales

Abstract Different jurisdictions use the AMA Guides to the Evaluation of Permanent Impairment (AMA Guides) for different purposes, and this article reviews a specific jurisdictional definition in the Province of Ontario of catastrophic impairment that incorporates the AMA Guides. In Ontario, a whole person impairment (WPI) exceeding 54% or a mental or behavioral impairment of Class 4 or 5 qualifies the individual for catastrophic benefits, and individuals who do not meet the test receive a lesser benefit. By inference, this establishes a parity threshold among dissimilar injuries and dissimilar outcome assessment scales for benefits. In Ontario, the Glasgow Coma Scale (GCS) identifies patients who have a high probability of death or of severely disabled survival. The GCS recognizes gradations of vegetative state and disability, but translating the gradations for rating individual impairment on ordinal scales into a method of assessing percentage impairments cannot be done reliably, as explained in the AMA Guides, Fifth Edition. The AMA Guides also notes that mental and behavioral impairment in Class 4 (marked impairment) or 5 (extreme impairment) indicates “catastrophic impairment” by significantly impeding useful functioning (Class 4) or significantly impeding useful functioning and implying complete dependency on another person for care (Class 5). Translating the AMA Guides guidelines into ordinal scales cannot be done reliably.

Review of One to One: The Experience of Psychotherapy.

1989 ◽  
Vol 34 (10) ◽  
pp. 958-958
Author(s):  
No authorship indicated
Keyword(s):  
One To One

A Multivariate Approach to Analyze Ordinal Data Collected in Clinical Trials of Analgesic Agents

1979 ◽  
Vol 18 (03) ◽  
pp. 175-179
Author(s):  
E. Mabubini ◽  
M. Rainisio ◽  
V. Mandelli
Keyword(s):  
Clinical Trials ◽  
Multivariate Analysis ◽  
Analgesic Effect ◽  
Ordinal Data ◽  
Ordinal Scale ◽  
Multivariate Approach ◽  
Controlled Clinical Trials ◽  
Second Stage ◽  
Analgesic Agents ◽  
Relevant Factors

After pointing out the drawbacks of the approach commonly used to analyze the data collected in controlled clinical trials carried out to evaluate the analgesic effect of potential agents, the authors suggest a procedure suitable for analyzing data coded according to an ordinal scale. In the first stage a multivariate analysis is carried out on the codec! data and the projection of each result in the space of the most relevant factors is obtained. In the second stage the whole set of these values is processed by distribution-free tests. The procedure has been applied to data previously published by VENTAITBIDDA et al. [18].

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