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§ Compared with patients in the white/not recorded group *n/a as not included in the final model Models also included fractional polynomial terms for age, body mass index and systolic blood pressure.
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Our final adjusted model (model D) also demonstrated an independent association between NT-pro-BNP and all-cause mortality (HR (95% CI) 2.15 (1.19 to 3.88)).
The best RNN model (model 10) is also compared with an equation based on the Shannon entropy.
In a fourth trait model (Model D), we also obtained power over mixed types of causal models with variable effect sizes.
In the final model (model 3), we also added terms for gestational age, offspring pubertal stage, and BMI as potential mediators.
Using the National Health and Nutrition Examination Survey for 1999−2004, the authors compared logistic regression models with established risk factors (model 1) with a model (model 2) that also included familial risk of diabetes (average, moderate, and high).
Models other than polynomial models also sometimes can be more meaningful.
A closely related notion is the one of probing models (also 'study models' or 'toy models').
Unlike the Bergeron model, these models also represent the total system resistance R as a distributed parameter (along with a distributed system L and C), providing a much more accurate representation of attenuation.
Since the system which provided the initial implementation for Model 2 had a significantly different purpose and implementation than Model 1, the final models also contained significant differences.
Interestingly, weather models also outperformed climate models for sedentary species.
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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