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In the feedback to the Belgian College of Physicians, we present an "Average" and two "Outlying" categories of hospitals.
Owing to the small number of participants in the outlying categories, thinness and severe thinness (approximately 1% of the entire population) were combined with normal weight.
The Tucker1 method organized the outlying categories into four clusters as shown in Table 4 which we shall interpret at the Discussion section in detail.
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For the period analysis, a first, outlying category, the 'high AMI-CFR' group, consists of those hospitals with a departure of >+35% and statistically significant (Bonferroni-corrected).
A first, outlying category, the 'high CSR' group, consists of those hospitals with a departure of > +35 and statistically significant (Bonferroni-corrected).
A second, outlying category, the 'low AMI-CFR' group, consists of those hospitals with a departure of <-25% and statistically significant (Bonferroni-corrected).
For this purpose, municipalities were grouped into three categories: Brindisi, neighboring municipalities and outlying municipalities in Brindisi province.
This comparison led us to conclude that the improved fit from the 2PL model led to minimal reclassification of subjects, with respect to the subject scores estimated from the 1PL model, because overlap of 2PL scores between 1PL score categories was only observed for small numbers of outlying values.
First, we divided the non-urban region into two categories, namely, candidate region for adjacent pattern urban growth (CRFAP) and candidate region for outlying pattern urban growth (CRFOP).
Used in outlying districts first, then in congested districts.
There have been outlying results on either side.
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