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However, it full-fills other favorable characteristics of validation measures listed by Nicholls [24] as it has no free parameters, and can be estimated in a robust way providing confidence intervals [25].
The baseline characteristics of validation and test cohorts are presented in table 2.
Characteristics of validation studies that were abstracted included the study population, health condition, topic of the validation (e.g., single diagnosis code versus case-finding algorithm), administrative and validation data sources.
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The clinical characteristics of the validation cohort were not significantly different from those of the derivation cohort, except that in the validation cohort the patients with SSAKI had a higher proportion of males and a lower proportion of patients with negative cultures compared with the patients without SSAKI (Table 4).
Additional points were awarded based on the characteristics of the validation sample, where "1" was given to studies where n > 300, as this is recommended for scale validation [ 23], and "0.5" where sample sizes were between 200 and 299.
If the characteristics of the validation data are similar in information content and variability to those in the calibration period, we term this an "interpolation case", and expect the model errors during calibration to be similar to those in validation.
The characteristics of the validation set were compared to those of the test set.
The patient characteristics of the validation sample are given in Table 1.
The characteristics of the validation set are likewise presented in Table 1.
The baseline characteristics of the validation study subjects are shown in Table 1.
Clinical characteristics of the validation cohort are summarized in Table 1.
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