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We investigated whether a risk factor that minimally improves the AUC may nevertheless improve the predictive ability of the model, assessed by integrated discrimination improvement (IDI).
These conditions were then modelled using a finite difference mathematical model and the accuracy of the model assessed by comparison with the experimental results.
Despite high receptor expression and homogenous receptor density in our model, assessed by biodistribution and in vitro autoradiography [21], high-resolution in vivo SPECT imaging revealed non-uniform uptake patterns of radioactivity within tumor tissue [20, 22, 23].
The overall discrimination performance of this model assessed, by a ROC curve, was 0.72 (95% CI (0.69 0.75)).
The overall fit of the model, assessed by the comparative fit index, was good [CFI = 0.97, root mean square error of approximation (RMSEA) = 0.05, χ/d.f. = 1.85].
The reproducibility of the model assessed by bootstrap re-sampling was very good: about 98% of the models chosen included FAMHXMOLES, 71%and70%0% included NMSC and SKINCOLOUR, respectively, and 61% included MOLES_RARM.
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No detrimental effects of high Ca were observed on bone modeling (assessed by histomorphometry and bone markers), at least in this short-term intervention.
An additional version of the single factor model was assessed by modelling correlated error terms for the negatively worded items [ 22].
The predictive value of each model was assessed by the model likelihood ratio χ2 statistic.
The predictive value of each model was assessed by the model likelihood ratio chi-square statistic.
The quality of the model is assessed by comparing the modeled and measured log data as shown in Fig. 17.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com