scholarly journals Current ERCP practice in Belgium: the BSGIE survey

2021 ◽  
Vol 84 (1) ◽  
pp. 73-77
Author(s):  
C Snauwaert ◽  
X Dekoninck ◽  
T Moreels
Keyword(s):  
Informed Consent ◽  
Real Life ◽  
Daily Practice ◽  
Current Status ◽  
Case Volume ◽  
Belgian Society ◽  
Real Life Data ◽  
Measurement And Evaluation ◽  
Written Informed Consent

Background and study aims : Data on procedural outcome and quality of endoscopic retrograde cholangiopancreatography (ERCP) in Belgian practice are scarce. The aim of this study is to assess current status of ERCP-performance in Belgium. Methods : National multi-institutional survey (online question-naire) among members of the Belgian Society of Gastrointestinal Endoscopy (BSGIE), conducted in the period June-August 2018. The RIZIV/INAMI provided real-life data on the total number of ERCPs performed in Belgium. Results : Forty-five responders completed the survey (for43 centers performing ERCP), providing information for 8368 ERCPs performed in 45% (43/95) of institutions performing ERCP in Belgium. Fifty-eight percent of centers performed > 100 ERCPs/year and 7% of centers (n=3) performed < 50 ERCPs/year. According to the RIZIV/INAMI data, low case-volume centers are underrepresented in this survey. The most common ERCP-indication was stone extraction (52%). 74% of endoscopists had more than 10 years of experience in performing ERCP. The majority of centers had their own written protocol (84%) for microbiological duodenoscope surveillance. Monitoring of cannulation rate and post-ERCP pancreatitis (PEP) was only performed in a minority of centers (30%). The majority of centers (76%) provided verbal informed consent relating to the ERCP-procedure ; a minority also requested a written informed consent (23%). 65% of centers systematically use NSAIDs for PEP-prophylaxis. Conclusion : This is the first survey of ERCP performance in Belgium. There were wide variations in practice. Adherence to key performance measures and measurement and evaluation of ERCP performance in daily practice at center and endoscopist level are not uniformly widespread.

Mining Calendar-based Periodic Patterns from Nonbinary Transactions

2014 ◽  
Vol 23 (3) ◽  
pp. 277-291 ◽  
Author(s):  
Jhimli Adhikari
Keyword(s):  
Real Life ◽  
Frequent Itemsets ◽  
Periodic Pattern ◽  
Temporal Data ◽  
Certainty Factor ◽  
Periodic Patterns ◽  
Business Decisions ◽  
Life Data ◽  
Real Life Data

AbstractA large class of problems deals with temporal data. Identifying temporal patterns in these datasets is a natural as well as an important task. In recent times, researchers have reported an algorithm for finding calendar-based periodic pattern in time-stamped data without considering the purchased quantities of the items. However, most of the real-life databases are nonbinary, and therefore, exploring various calendar-based patterns (yearly, monthly, weekly, daily) with their purchased quantities may discover information useful to improve the quality of business decisions. In this article, a technique is proposed to extract calendar-based periodic patterns from nonbinary transactions. In this connection, the concept of certainty factor has been introduced by incorporating transaction frequency for overlapped intervals. Algorithms have been designed to mine frequent itemsets along with intervals and quantity. In addition to that, we have designed an algorithm to find the periodicity of the pattern. The algorithm is tested with real-life data, and the results are given.

scholarly journals 1533P Real-life data from the platform for outcome, quality of life and translational research on pancreatic cancer - PARAGON

2020 ◽  
Vol 31 ◽  
pp. S943
Author(s):  
S-E. Al-Batran ◽  
W. Blau ◽  
R. Liersch ◽  
S. Mahlmann ◽  
A. Lueck ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Pancreatic Cancer ◽  
Translational Research ◽  
Real Life ◽  
Outcome Quality ◽  
Life Data ◽  
Real Life Data

Improving the quality of the consenting and education process for patients starting on oral oncology medications.

2016 ◽  
Vol 34 (7_suppl) ◽  
pp. 82-82
Author(s):  
Lauren Azure Zatarain ◽  
Jessica Ashford ◽  
David Bivens
Keyword(s):  
Informed Consent ◽  
Patient Education ◽  
Clinic Visit ◽  
Health Record ◽  
Management Problem ◽  
Safety Standards ◽  
Oral Oncology ◽  
Written Informed Consent ◽  
Education Process

82 Background: Oral oncology medication prescribing is on the rise nationwide. Given that these medications are self-administered, drug compliance is a concern. Appropriate patient education directly impacts drug adherence. Prior to the process improvement initiative in our community based medical oncology clinic, implied consent was obtained while educating patients on side effects and written informed consent was documented 0% of the time. This creates a patient safety and risk management problem. Methods: Our clinic implemented a new process for consenting and educating patients starting on new oral oncology medications from April – October 2015. The plan/do/study/act performance improvement technique was utilized. A new consent form was created for oral oncology medications meeting ASCO/ONS safety standards. The consent and education packets were placed centrally in the office and coincided with an initiative to improve consenting for IV chemotherapy. Providers were in-serviced to contact the nurse prior to patients exiting clinic to initiate the new prescription and begin the patient education process. Reminder checklists were placed in exam rooms to trigger the provider to communicate with nursing staff. Outcome measures included data on the percent of patients with written informed consent in health record and percent of patients with patient education handouts in health record prior to drug start. Data on percent of patients for whom the provider notified nurse prior to end of clinic visit were collected as a balance measure. Results: Over a 6 month period, 101 consecutive patients initiated on oral oncology drugs were included in the data analysis. The baseline mean improved from 0% to 75% for obtaining written informed consent (p chart, 3 sigma). The baseline mean improved from 37% to 80% for documenting patient education in health record prior to oral oncology medication start (p chart, 3 sigma). Conclusions: There was significant improvement in obtaining written informed consent and providing patient education prior to oral oncology drug start after implementation of new clinic processes.

scholarly journals Corruption of the Pearson correlation coefficient by measurement error: estimation, bias, and correction under different error models

2019 ◽  
Author(s):  
Edoardo Saccenti ◽  
Margriet H. W. B. Hendriks ◽  
Age K. Smilde
Keyword(s):  
Pearson Correlation ◽  
Measurement Techniques ◽  
Life Sciences ◽  
Real Life ◽  
Correlation Coefficients ◽  
Gene Expressions ◽  
Science Data ◽  
Real Life Data ◽  
Comprehensive Measurement

ABSTRACTCorrelation coefficients are abundantly used in the life sciences. Their use can be limited to simple exploratory analysis or to construct association networks for visualization but they are also basic ingredients for sophisticated multivariate data analysis methods. It is therefore important to have reliable estimates for correlation coefficients. In modern life sciences, comprehensive measurement techniques are used to measure metabolites, proteins, gene-expressions and other types of data. All these measurement techniques have errors. Whereas in the old days, with simple measurements, the errors were also simple, that is not the case anymore. Errors are heterogeneous, non-constant and not independent. This hampers the quality of the estimated correlation coefficients seriously. We will discuss the different types of errors as present in modern comprehensive life science data and show with theory, simulations and real-life data how these affect the correlation coefficients. We will briefly discuss ways to improve the estimation of such coefficients.

scholarly journals The Use of HSQoL-24 in an Assessment of Quality-of-Life Impairment among Hidradenitis Suppurativa Patients: First Look at Real-Life Data

2021 ◽  
Vol 10 (22) ◽  
pp. 5446
Author(s):  
Piotr K. Krajewski ◽  
Servando E. Marrón ◽  
Manuel Gomez-Barrera ◽  
Lucía Tomas-Aragones ◽  
Yolanda Gilaberte-Calzada ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Hidradenitis Suppurativa ◽  
Disease Onset ◽  
Real Life ◽  
Clinical Settings ◽  
Life Data ◽  
Real Life Data ◽  
Inflammatory Skin Disorder ◽  
Family House

Hidradenitis suppurativa (HS) is a chronic inflammatory skin disorder with well-documented effects on patients’ quality of life (QoL). The aim of this study was to evaluate the QoL of patients with HS via the use of a newly developed questionnaire: Hidradenitis Suppurativa Quality of Life-24 (HSQoL-24). This study was performed on a population of 342 HS patients. Their QoL was assessed via the HSQoL-24 questionnaire. The perceived impairment of QoL due to HS in the studied group was considered to be serious (mean HSQoL-24 score: 58.3 ± 21.0 points). Women tended to experience a significantly higher impact from the disease than men (61.6 ± 19.2 points vs. 51.1 ± 23.1 points, p < 0.001). The HS severity had an effect on the perceived QoL, with statistically significant differences being evident between the self-assessed HS severity groups. The level of QoL impairment correlated positively with the number of affected body areas (r = 0.285, p < 0.001) and the duration of the disease (r = 0.173, p = 0.001), while the patients’ age at disease onset correlated negatively with the HSQoL-24 global score (r = −0.182, p = 0.001). Patients living in their family house scored higher than other groups. The least affected were patients who lived alone. The study shows that the HSQoL-24 questionnaire is a reliable, HS-specific tool for measuring the QoL among patients with HS in real-life clinical settings.

scholarly journals Answering the Min-Cost Quality-Aware Query on Multi-Sources in Sensor-Cloud Systems

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4486 ◽  
Author(s):  
Mohan Li ◽  
Yanbin Sun ◽  
Yu Jiang ◽  
Zhihong Tian
Keyword(s):  
Low Cost ◽  
Real Life ◽  
Quality Data ◽  
Parameter Setting ◽  
Multiple Sources ◽  
High Quality ◽  
High Quality Data ◽  
Real Life Data ◽  
Sensor Cloud

In sensor-based systems, the data of an object is often provided by multiple sources. Since the data quality of these sources might be different, when querying the observations, it is necessary to carefully select the sources to make sure that high quality data is accessed. A solution is to perform a quality evaluation in the cloud and select a set of high-quality, low-cost data sources (i.e., sensors or small sensor networks) that can answer queries. This paper studies the problem of min-cost quality-aware query which aims to find high quality results from multi-sources with the minimized cost. The measurement of the query results is provided, and two methods for answering min-cost quality-aware query are proposed. How to get a reasonable parameter setting is also discussed. Experiments on real-life data verify that the proposed techniques are efficient and effective.

Prediction of the Quality of Fresh Water in a Basin

2017 ◽  
pp. 351-367
Author(s):  
Sira M. Allende ◽  
Daniel C. Chen ◽  
Carlos N. Bouza ◽  
Agustin Santiago ◽  
Jose Maclovio Sautto
Keyword(s):  
Fresh Water ◽  
Rough Sets ◽  
Real Life ◽  
Regression Function ◽  
Computing Methods ◽  
Parametric Regression ◽  
Conditional Expectations ◽  
Real Life Data ◽  
Soft Computing Methods

Derivatives play an important role in social and economic studies. They describe the behavior of conditional expectations. Once a phenomena is characterized by parametric specifications, the conditional expectation m(x) may be modeled by a regression function. Then, derivatives may be computed by fitting the regression function. In applications, parametric estimators are commonly used, because of the un-knowledge of other more effective methods. The validity of a regression fitting approach depends on the knowledge of certain aspects related with the true functional form. In this paper, we develop a study on the usage of soft computing methods for providing an alternative to the use of non-parametric regression. We develop our modeling including neural networks and rough sets approaches. The studied problem is the eutrophication due to the growth of the population of algae. Real life data is provided by a study on a fresh water basin. They are used for developing a comparison of different approaches. A methodology is recommended for implementing a monitoring system of the water quality.

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