Ibañez scoring system for complication classification is another important scoring categorization for predict outcome from complication.
In addition to our exploration of individual card sort rules and their use by experts and novices, we quantified card sort performance by scoring categorization features as expert (+1) or novice (−1; as discussed above and illustrated in Table 1).
Regarding the visual scores categorization was based on the MLO image alone for these four women as would be the case in a clinical setting.
The minimum RMDQ score for categorization as LBP was 3. Logistic regression was used to select significant factors to build a risk score.
Prediction: Coalition Members Will be Categorized by Tenure Length As predicted, subjects implicitly categorized members by tenure length: The average categorization score was significantly greater than zero (M = 0.78, SD = 1.77, t53 = 3.24, one-tailed p = 0.001, r = 0.41).
For each subject we calculate a single categorization score, which indexes the degree to which subjects implicitly categorize by the four tenure lengths.
This approach should lead to the definition of a risk score, allowing the categorization of patients according to their risk for structural or clinical progression.
We then subtract the corrected number of between-category confusions from the total number of within-category confusions to create a categorization score.
