The linear model (solid line) given by Eq 3 was selected as the most parsimonious model in our analysis. The models of Abduljalil et al. [28] and Gaohua et al. [29], both quadratic, were calibrated using the same curated data set [28] used by us. The latter of these models was modified as described in the text. The model of Luecke et al. [15] was calibrated using different data. It predicts maternal fat mass as a function of total fetal mass, and was interpreted as described in the text. Dallmann et al. [3] also selected a linear model, but they calibrated their model with different data.</p
The logistic model (solid line) given by Eq 50 was selected as the most parsimonious model in our an...
<p>Results from univariate and multiple linear regression using difference in abdominal circumferenc...
<p>Results from univariate and multiple linear regression using difference in estimated weight perce...
The cubic model (solid line) given by Eq 1 was selected as the most parsimonious model in our analys...
The modified logistic model (solid line) given by Eq 5 was selected as the most parsimonious model i...
The quadratic model (solid line) given by Eq 24 was selected as the most parsimonious model in our a...
The Gompertz model (solid line) given by Eq 2 was selected as the most parsimonious model in our ana...
The quadratic growth model (solid line) given by Eq 28 was selected as the most parsimonious model i...
The cubic growth model (solid line) given by Eq 31 was selected as the most parsimonious model in ou...
The cubic model (solid line) given by Eq 13 was selected as the most parsimonious model in our analy...
The modified logistic model (solid line) given by Eq 6 was selected as the most parsimonious model i...
The Gompertz model (solid line) given by Eq 41 was selected as the most parsimonious model in our an...
The Gompertz model (solid line) given by Eq 39 was selected as the most parsimonious model in our an...
The quadratic model (solid line) given by Eq 26 was selected as the most parsimonious model in our a...
The Gompertz model (solid line) given by Eq 37 was selected as the most parsimonious model in our an...
The logistic model (solid line) given by Eq 50 was selected as the most parsimonious model in our an...
<p>Results from univariate and multiple linear regression using difference in abdominal circumferenc...
<p>Results from univariate and multiple linear regression using difference in estimated weight perce...
The cubic model (solid line) given by Eq 1 was selected as the most parsimonious model in our analys...
The modified logistic model (solid line) given by Eq 5 was selected as the most parsimonious model i...
The quadratic model (solid line) given by Eq 24 was selected as the most parsimonious model in our a...
The Gompertz model (solid line) given by Eq 2 was selected as the most parsimonious model in our ana...
The quadratic growth model (solid line) given by Eq 28 was selected as the most parsimonious model i...
The cubic growth model (solid line) given by Eq 31 was selected as the most parsimonious model in ou...
The cubic model (solid line) given by Eq 13 was selected as the most parsimonious model in our analy...
The modified logistic model (solid line) given by Eq 6 was selected as the most parsimonious model i...
The Gompertz model (solid line) given by Eq 41 was selected as the most parsimonious model in our an...
The Gompertz model (solid line) given by Eq 39 was selected as the most parsimonious model in our an...
The quadratic model (solid line) given by Eq 26 was selected as the most parsimonious model in our a...
The Gompertz model (solid line) given by Eq 37 was selected as the most parsimonious model in our an...
The logistic model (solid line) given by Eq 50 was selected as the most parsimonious model in our an...
<p>Results from univariate and multiple linear regression using difference in abdominal circumferenc...
<p>Results from univariate and multiple linear regression using difference in estimated weight perce...