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Comparing the relationship curves of both studies, the findings of present study demonstrated more information: (1) the initial increase of CSA cannot be observed in previous study because it occurred below the first contraction level; and (2) the reliability of the polynomial regression was improved since there were much more sample points in the present study.
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The model fit (adjusted R2) of the regression models was improved as plot size increased.
The model fit (adjusted R) of the regression models was improved as plot size increased.
By adding the clinicians as a factor in the multi-level regression model, the model fit was improved.
Model performance was improved by autologistic regression, and models restricted to widely available data were only slightly poorer.
Performance in outcome prediction was improved over logistic regression at day 3 - sensitivity 86%, specificity 81%, AUROC 0.91 (0.89 to 0.93, P < 0.001 for all comparisons) in a model incorporating 30 variables.
The regression coefficients are improved after annealing at 300 and 500 °C.
We further note that the predictive ability of simpler methods, such as linear regression, could be improved using additional physics-based constraints.
All trait-climate regression models were improved by incorporation of phylogenetic relationships (Table 1; Figure 2), as demonstrated by the much lower AIC scores for phylogenetic GLS versus non-phylogenetic GLS models for all traits.
We found that the regression can be improved through a logarithmic transformation on the data.
However, the regressions could be improved in future with larger sample size to better represent the diversity of the population.
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