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Conclusions: Our results show that information on client characteristics available from inpatient stay records is useful in predicting not only the length of inpatient stay but also the length of the subsequent community stay.
We examined the following family characteristics available from the census for their association with incidence of ALL: maternal and paternal age at birth of the child (in four categories: <25, 25 29, 30 34 and ≥35 years), and number of older children living in the same household as the study participants (0, 1 or ≥2), respectively.
Subsets of CRC cases selected were based on available data concerning various characteristics available from tumor records in the UCR.
Strength of this study was the relatively large number of patient characteristics available from the electronic medical records.
Personal characteristics available from the data sources included age at death, sex, marital status, country of birth, need for interpreter service, and causes of death.
Cox's proportional hazards model was used to investigate the relationship between survival time and baseline patient and provider characteristics available from the NHI data.
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The two PM2.5 mass and composition data sets employed in the source apportionments were selected based on their ready availability for analysis, the similar availability of a compatible daily mortality record for health effects analysis, and the fact that their PM2.5 composition analyses were similar in many ways to those characteristics available to researchers from the new U.S. EPA PM2.5 STN.
Pregnancy and birth characteristics, parental characteristics, children's lifestyle and health characteristics were available from 1714 parental questionnaires.
Inclusion criteria were age >18 years, palpable cT1 (as measured by preoperative ultrasound and mammography), unifocal invasive breast carcinoma, tumor visible on ultrasound, preoperative tumor characteristics available, and distance of ≥1 cm from the thoracic wall and the overlying skin.
The ERα use case, as well other examples demonstrating the use of InCHlib for the exploration of protein structures, identification of gene expression patterns or classification of whiskies based on their taste characteristics, are available from http://openscreen.cz/software/inchlib/use_cases/13.
Disease and intervention characteristics are available from profiles reported in the literature [29] and on the Internet (https://www.epimodels.org/midas/modelProfilesFull.do).do
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