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To compare the performance of actors sets selected by the HT measure with other influence measures, we selected sets of top actors based on the HT, T, PageRank, and Katz centrality measures.
To compare the performance of actors sets selected by the T measure with other influence measures, we selected sets of top actors based on the T measure and sets identified by measures that are known to be good heuristics for seed set selection, namely degree and betweenness centrality [26].
It is possible that the outcome measures we selected were not sensitive to changes which did occur.
When a paper reported multiple models with different income inequality measures, we selected the analyses using Gini coefficient, the most commonly used measure of income inequality (see box).
As secondary outcome (process measures) we selected degree of adherence to safety procedures, e.g. maximum sterile barrier during the CVC insertion procedure and adherence to hygienic protocols during intravenous administration.
For each of the three stress measures, we selected a subset of the original items for brevity based on analyses of other pregnancy data sets indicating that these items were most predictive of adverse birth outcomes (Rini et al. 1999).
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Based on each feature selection measure we selected the top 50, 100, 200 and 300 features.
We identified the 2 short-form items in Sample 1 and confirmed their properties in Sample 2. In order to identify the 2 items for the short-form measure, we selected the first item based on the highest item-total correlation.
Along with the computation of the measure, we selected an approach to present results graphically for each measure.
Based on the observations and the PARP activity measured we selected the HepG2 (intermediate PARP activity and sensitive to ABT-888) and PLC-PRF-5 (low PARP activity and resistant to ABT-888) cell lines for the further experiments combining radiation exposure with ABT-888 treatment.
Although a variety of co-morbidity measures exist, we selected the Charlson index as it is widely understood and most commonly used.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com