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Patients were classified according to the worst RIFLE category achieved while in ICU, with RIFLE categories Injury and Failure used to define AKI.
No single category achieved a significance score indicating statistically significant over-representation of a specific gene ontology, and several gene ontology classes showed multiple genes (Additional File 2).
Patients in these four cohorts were followed in the LTE studies and reclassified into the same categories, based on the maximum percentage increase category achieved in two consecutive SCr measurements.
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Both companies' apps are free on Android and Apple, and both have improved significantly over the years, to the point where their competition helped the mobile real estate category achieve a level of sophistication lacking in many other parts of the app market.
The coefficient of variation confirms this, with no industry category achieving predominantly lower or higher variability across variables.
Another reason comes from analyzing RCPs from 9 to 12, where it is observed that the Normal (and in many cases Unknown) category achieves similar levels to a certain fault category.
Each row shows the proportion of patients within each category achieving the indicated number of parameters at goal.
The dependent variables used were number of categories achieved (a measure of rule discovery) and number of perseverative errors.
The effect of allelic variation in ANK3 was negligible for FSIQ (Cohen's d = 0.14), WCST categories achieved and perseverative errors (Cohen's d = 0.05 for both) and IGT (Cohen's d = 0.02).
Most of the entity categories achieved greater than 90% F-values.
All 11 QOL categories achieved significance (p <0.001 – see Table 1).
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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