To obtain an empirical threshold for the significance of each test, we used the maximum and minimum scores obtained for the neutral regions in each population.
To make evaluation practical and for simplicity sake, we ignore such errors in measurement, although there are statistical methods that address this problem [ 14] (Table 1).> -wrap-foot> aListing of items for each scale, and minimum and maximum scores obtained from Lam and Petri [ 1] We can simplify an ordinal scale to form a binary outcome.
The Z-scores results for the comparison between the Hypothetical Brain and the Real brains of the studied population are shown in figure 1 and they clearly demonstrate that entropy values associated with the different types of brain are significantly different, since the minimum Z-score obtained was 2.46.
In order to transform the total score into a metric with a minimum at 0 and a maximum at 100 (the most intuitive and easy to interpret metric), the following formula can be used: Y ′ = Y o b s − Y min Y max − Y min × 100 = (Y o b s − 17 ) × 1, 961 Where Ymax = 68 (maximum total score); Ymin = 17 (minimum total score); Yobs = total score obtained by the patient, Y' = transformed score.
With regards to the methodological quality of the studies, the mean score obtained with the quality scale (range: 0 8) was 6.1 (minimum: 3.4, maximum: 7.5).
An RSAT score of 4.5 was used as a minimum cut-off value in this analysis because this is the lowest score obtained for a bona fide ZRE found in the EKI1 promoter [ 21].
As detailed in the AGREE-II manual, we calculated a percentage score for each domain using the following formula; ((score obtained by raters−minimum score possible)/(maximum score possible−minimum score possible) × 100), such that the maximum achievable score is 100% and the minimum achievable score is 0%.
The minimum and maximum mean scores obtained in various statements were 1.15 and 3.95 respectively.
Box plots and whiskers represented the median, first and third quartiles, minimum, maximum and outliers of scores obtained by expert and non-expert ratings of the 13 statements.
To increase robustness of the classification to local minima of the inference process, scores obtained for the same sequence from several grammars trained for the same class could be averaged in order to produce a combined grammar classifier.
