# Cephalometric Evaluation of Facial Height Ratios and Growth Patterns: A Retrospective Cohort Study
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Model Coefficients: Divergent**} & \multicolumn{4}{c}{**95\% Confidence Interval**} \\ \hline
**Divergent** & **Predictor** & **Estimate** & **SE** & **Z** & **_p_** & **Odds Ratio** & **Lower** & **Upper** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Hyperdivergent- \\ Normodivergent \\ \end{tabular} } & Intercept & 24.8994 & 12.1376 & \(-\)2.051 & 0.040 & \(1.54\times 10^{-11}\) & \(7.16\times 10^{-22}\) & 0.330 \\ \cline{2-9}  & Age & \(-\)0.0564 & 0.1059 & \(-\)0.532 & 0.594 & 0.945 & 0.768 & 1.163 \\ \cline{2-9}  & \(Y\)-axis\_to\_SN & 0.3547 & 0.1732 & 2.047 & 0.041 & 1.426 & 1.015 & 2.002 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Hypodivergent- \\ Normodivergent \\ \end{tabular} } & Intercept & 21.0955 & 7.1048 & 2.969 & 0.003 & \(1.45\times 10^{9}\) & 1300.356 & \(1.62\times 10^{15}\) \\ \cline{2-9}  & Age & 0.0475 & 0.0468 & 1.015 & 0.310 & 1.049 & 0.957 & 1.149 \\ \cline{2-9}  & \(Y\)-axis to\_SN & \(-\)0.3050 & 0.1052 & \(-\)2.900 & 0.004 & 0.737 & 0.600 & 0.906 \\ \hline \multicolumn{9}{c}{SE: standard error.} \\ \end{tabular}
\end{table} 
Table 6: Predictors of divergence type based on anthropometric measurements. divergent group. We also saw differences between males and females in AFH, PFH, and Jarabak's ratio in the hypodivergent group. This aligns with findings indicating the lowest sexual dimorphism in Class I malocclusion, as noted by [PERSON] and [PERSON] [18]. When we compared our findings with those of [PERSON] and [PERSON] [18], it was evident that there were disparities in PFH, AFH, and FHR among different malocclusions. We found that Class I malocclusion had a TAFH of 112.0 (IQR: 104.0 to 122.8), a TPFH of 77.0 (IQR: 71.2 to 81.0), and a \(y\)-axis to SN of 65.0 (IQR: 61.2 to 69.0). Class II malocclusion had a TAFH of 112.0 (IQR: 104.0 to 119.1), a TPFH of 75.0 (IQR: 69.0 to 81.1), and a \(y\)-axis to SN of 65.0 (IQR: 62.0 to 69.0). Additionally, Class III malocclusion displayed a TAFH of 113.0 (IQR: 107.0, 116.3), TPFH of 76.0 (IQR: 70.7, 78.3), and \(y\)-axis to SN of 67.0 (IQR: 59.7, 69.0). Notably, our study identified different distributions of malocclusions among growth patterns for females and males, showcasing variations across different classifications. While our findings broadly align with [PERSON] and [PERSON]'s observations, discrepancies exist, highlighting potential racial variations and underscoring the need for further investigation to elucidate the underlying mechanisms. Overall, our study adds valuable insights into the gender-specific distribution of growth patterns and further delineates the relationship between cephalometric parameters and malocclusions. Future research with larger cohorts is necessary to validate and enhance our findings, offering a comprehensive understanding of the intricate interplay between gender, cephalometric parameters, and growth patterns in craniofacial development. Additionally, it could be interesting to include the integration of recently introduced technologies such as artificial intelligence [34] and smartphone applications [35]. Our study's correlation analysis between cephalometric measurements yielded valuable insights into craniofacial relationships. We observed strong negative correlations between the Jarabak ratio and the \(y\)-axis to SN angle, suggesting a dynamic interplay between facial height and mandibular position. Additionally, we found moderate positive correlations between the \(y\)-axis to SN and TAFH, as well as between TPFH and TAFH. These findings underscore the intricate nature of craniofacial morphology, emphasizing the importance of employing comprehensive assessment methods. Our findings align with previous research by [PERSON] et al., who reported \(y\)-axis to SN angles of 63.67 \(\pm\) 3.39\({}^{\circ}\) for Class I males and 64.74 \(\pm\) 3.61\({}^{\circ}\) for females [21].