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All but one of the misspecified models under cut-off p = 0.0001 were one-step errors.
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Table 3 compares the validity of the risk score model under different cut-off values (based on individual total risk scores) in different groups.
For both models, the cut-off values constituted the largest sum of the sensitivity + specificity.
Firstly, we tested the NCGC Jurkat IC50 models (FCFP_6, toxicity cut-off pIC50 > 4.64) against the Scripps IC50 dataset (FCFP_6, toxicity cut-off pIC50 > 5.5).
bArea under the receiver operating characteristic curve (AUC) based on 28-day mortality outcome in logistic regression models with the cut-off as the univariate independent variable; this is a combined measure of sensitivity and specificity.
The Bayes ensemble model with cut-off (=0.4) has a (kappa > 0.62) for both the datasets.
The best fit model was obtained in the model including the cut-off value of ARC detection by CKD-EPI.
In case of Bayes ensemble model, varying the cut-off leads to perfect sensitivity (cut-off = 0) or perfect specificity (cut-off = 1).
For each model we determined cut-off values and classification rules to achieve a sensitivity > 0.95.
We check the improvement provided by the model for different cut-off probabilities (Fig. 4).
The quality criterion of the model had a cut-off of 14 (>2), suggesting a relevant classification.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com