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When adding the scores to the baseline risk model, the discrimination of Model + SIRS has no change, although improved with qSOFA, CRB and SOFA.
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Calibration and discrimination of models were performed.
The discrimination of models was quantified using Harrell's concordance index.
The discrimination of models was also compared to a c-statistic of 0.5, or the equivalent of predicting outcome based on the flip of a coin, using chi-square tests.
The discrimination of models was quantified using Harrell's concordance index (Harrell et al, 1996), which is similar to the area under the curve for receiver operating characteristics curve but allows calculation in continuous and censored data (such as time to event data).
This result indicated that compared with the Tepas 1987 model, the discrimination of our model was statistically superior.
First, we used a simulation procedure to investigate the effect of the predictive accuracy of the baseline model on the discrimination of the model updated with rare variants.
A value of 0.5 represents a chance prediction, values >0.8 are considered discriminative, and the discrimination of the model is improved as the C value approaches 1.0.
We used the area under the receiver operating curve (AUROC) to assess the discrimination of the model and Hosmer-Lemshow test to assess model calibration.
When the goodness of fit and discrimination of a model are tested using the data on which the model was developed, they are likely to be over-estimated.
We first fitted a model similar to our original predictive model to the 1993 Cohort sample and then we calculated the calibration and discrimination of the model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com