Sentence examples for taking a cutoff from inspiring English sources

By taking a cutoff point of 7 for the MASS score, a sensitivity of 65.7%, specificity of 37.5%, PPV of 89.8%, NPV of 11.5% and accuracy of 62.7% were calculated.

When taking a cutoff point of 3.0-cm tumour diameter, the mean difference of the cross-sectional area in the second ICS for reader 1 was 1.43 mm2 in the group with tumours ≤ 3.0 cm compared to 1.89 mm2 in the tumours bigger > 3.0 cm.

Taking a cutoff of >10% nuclear Snail expression in tumor cells, ElMoneim and Zaghloul found a significantly higher Snail expression in breast cancers (40.9%, n = 132) [ 13].

Taking a cutoff point of 0.05 on the predicted probability of neoplasia from the logistic regression model, all patients predicted as positive from this process were re-entered into a second logistic regression, where a cutoff of 0.05 was also taken as the most appropriate.

To ensure that our definition did not include children whose accuracy was achieved only by very slow reading, we used the timed subtests of the TOWRE (sight word efficiency and phonetic decoding efficiency), taking a cutoff of average score on the two subtests of 83 (i.e., below 13th percentile).

As the contribution to causality of each gene in regions with a large number of deleted or duplicated genes is difficult to assess, we decided to take a cutoff of the number of genes in the region (≤5 genes, Supplementary Figure 1), and therefore the genes present in regions that contained five genes or fewer were selected.

Notably, the relatively low numbers of PT-differentially regulated genes compared to those of PP may have occurred, despite taking a liberal cutoff (2 fold).

If Youden's index Y, i.e., true positive rate minus false positive rate, is maximal, then the classification accuracy cannot be further improved by taking a larger cutoff [ 19].

Data in the ROC-table (see Additional file 1: Table S1) show that taking an NGAL cutoff of >130 ng/ml achieves a high specificity (specificity 91%%) and a high positive predictive value (90%%) for stage 3 AKI.

After the assignment of regulators we took a stringent cutoff corresponding to the top 2% most significant predicted regulatory interactions (Figure 1), obtaining a final set of 294 unique regulators (the complete list of module genes and regulators is available in table S1).

Given the algorithmic difficulties in detecting structural variants we took a strict cutoff to minimize false positive calls (see Methods).