EVALUATION OF DATA FROM 1D-PSD USED IN TOF METHOD

1986 ◽  
Vol 47 (C5) ◽  
pp. C5-129-C5-136 ◽  
Author(s):  
N. NIIMURA
Keyword(s):  
Evaluation Of Data

Performance Evaluation of Data Transmission in Maritime Delay-Tolerant-Networks

2013 ◽  
Vol E96.B (6) ◽  
pp. 1435-1443 ◽  
Author(s):  
Shuang QIN ◽  
Gang FENG ◽  
Wenyi QIN ◽  
Yu GE ◽  
Jaya Shankar PATHMASUNTHARAM
Keyword(s):  
Performance Evaluation ◽  
Data Transmission ◽  
Delay Tolerant Networks ◽  
Delay Tolerant ◽  
Evaluation Of Data

scholarly journals Appropriateness of antibiotics for medical prophylaxis: evaluation of data from the Italian PPS-2

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
A Cremona ◽  
J Garlasco ◽  
I Gintoli ◽  
A D'Ambrosio ◽  
F Quattrocolo ◽  
...  
Keyword(s):  
Empirical Therapy ◽  
Infectious Complications ◽  
High Rate ◽  
National Guidelines ◽  
Prevalence Survey ◽  
Inappropriate Use ◽  
Healthcare Associated ◽  
Evaluation Of Data ◽  
The Eu ◽  
Made In

Abstract Background Antibiotics (AB) are administered for medical prophylaxis (MP) to prevent infectious complications. The second national point prevalence survey (PPS) of healthcare-associated infections and AB use was conducted in Italy in 2016-2017. The survey found MP accounted for nearly 25% of all registered ABs, more than twice the EU/EEA proportion. The aim of this study was to evaluate how many ABs were administered for MP and how frequently general indications were followed. Methods Data from the PPS were used to evaluate the prevalence of indications for MP over the total number of ABs, the proportion - among these indications - of those justified by a motivation in the patients' charts, and the prevalence of ABs considered appropriate for MP. The evaluation was made in 12 hospitals out of 14 participating in the PPS in Piedmont, chosen on the basis of patient traceability. According to national guidelines, the indications for MP that were considered appropriate in this study were: Trimethoprim/sulfamethoxazole for P. carinii pneumonia, Rifaximin for diverticulitis, Penicillin for Streptococcal infections and for splenectomized patients, and Rifampin for TB infections. Results 1844 AB prescriptions were registered among 1334 traceable inpatients in Piedmont. The prevalence of indications for MP was 16.2% and 253 ABs (84.6%) were prescribed with a motivation. Only 3% of ABs registered as MP were appropriate for this indication: Bactrim 2%, Rifaximin 0.3%, Penicillin 0.2% and Rifampin 0.4%. Conclusions This study found an extremely high rate of inappropriate ABs for MP, that will be further investigated through qualitative analysis of medical records to evaluate whether a misunderstanding of the PPS protocol occurred and empirical therapy was confused with MP. Nevertheless, this study highlighted the need for interventions to improve prescribing appropriateness for MP. Key messages This study found that only 2.98% of ABs registered as MP among traceable patients in Piedmont were appropriate for this indication. 15.38% of prescriptions for MP were not justified by a motivation. Interventions to improve prescribing appropriateness for MP could lead to a considerable reduction in inappropriate use of ABs, which is crucial in a country facing hyperendemic levels of AMR.

scholarly journals A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 875
Author(s):  
Jesus Cerquides ◽  
Mehmet Oğuz Mülâyim ◽  
Jerónimo Hernández-González ◽  
Amudha Ravi Shankar ◽  
Jose Luis Fernandez-Marquez
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Graphical Model ◽  
Real Life ◽  
Scientific Journals ◽  
Probabilistic Framework ◽  
Science Data ◽  
Label Aggregation ◽  
Evaluation Of Data ◽  
Model Formalism

Over the last decade, hundreds of thousands of volunteers have contributed to science by collecting or analyzing data. This public participation in science, also known as citizen science, has contributed to significant discoveries and led to publications in major scientific journals. However, little attention has been paid to data quality issues. In this work we argue that being able to determine the accuracy of data obtained by crowdsourcing is a fundamental question and we point out that, for many real-life scenarios, mathematical tools and processes for the evaluation of data quality are missing. We propose a probabilistic methodology for the evaluation of the accuracy of labeling data obtained by crowdsourcing in citizen science. The methodology builds on an abstract probabilistic graphical model formalism, which is shown to generalize some already existing label aggregation models. We show how to make practical use of the methodology through a comparison of data obtained from different citizen science communities analyzing the earthquake that took place in Albania in 2019.

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