## 3 Results
The remaining variables and their respective maxima for both the binary logistic regression and ordinal logistic regression models were Coma-Like Deviation (0.61 and 0.62), Q8 mm (0.55 and 0.57), Spherical-Like Deviation (0.49 and 0.55), Nasal Thickness (0.81 and 0.83), Superior Thickness (0.88 and 0.89), Inferior Thickness (0.72 and 0.77), Volume (0.69 and 0.71), Anterior Surface (0.83 and 0.89) and Posterior Surface (0.58 and 0.60). For both models, the variables of High-Order Aberration, CDVA, Central Thickness, Total RMS and Temporal Thickness have power values over 0.80 for the sample  
Figure 10: Distribution of the logistic regression scores between the Control and RETICS I patients, and the optimal cut-off. Figure 9: Logistic regression model ROC curve indicating the optimal cut-off point and the bootstrapped validation parameters. sizes exceeding 150, and the powers for the ordinal logistic model at small sample sizes (below 100) are somewhat lower than for the binary logistic regression models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **Std.** & \(t\) & \(p\) & & **95\% CI** \\ \hline
**Variables** & **Coefficient** & **Error** & **Value** & **Value** & **OR** & **Lower** & **Upper** \\ \hline
**Demographic** & & & & & & & \\ \hline Age & 0.016 & 0.011 & 1.475 & 0.140 & 1.016 & 0.995 & 1.037 \\ Gender & \(-\)0.073 & 0.361 & \(-\)0.203 & 0.839 & 0.929 & 0.458 & 1.885 \\ \hline
**Clinical** & & & & & & \\ \hline CDVA & \(-\)5.495 & 0.865 & \(-\)6.355 & 0.001 & 0.004 & 0.001 & 0.022 \\ Coma-like & 0.106 & 0.348 & 0.304 & 0.761 & 1.111 & 0.562 & 2.197 \\ QSmm & 0.184 & 0.359 & 0.512 & 0.609 & 1.201 & 0.595 & 2.423 \\ Spherical-like & \(-\)0.063 & 0.250 & \(-\)0.250 & 0.803 & 0.940 & 0.575 & 1.535 \\ RMS total & \(-\)0.422 & 0.298 & 1.416 & 0.157 & 1.526 & 0.850 & 2.737 \\ High-order & 0.621 & 0.345 & 1.797 & 0.072 & 1.860 & 0.945 & 3.660 \\ Astigmatism & \(-\)0.250 & 0.239 & \(-\)1.046 & 0.296 & 0.779 & 0.488 & 1.244 \\ \hline
**Pachymetry** & & & & & & \\ \hline Central thickness & \(-\)0.024 & 0.007 & \(-\)3.298 & 0.001 & 0.977 & 0.963 & 0.991 \\ Temporal & 0.006 & 0.002 & 2.662 & 0.008 & 1.006 & 1.002 & 1.010 \\ Nasal & \(-\)0.002 & 0.003 & \(-\)0.771 & 0.441 & 0.998 & 0.992 & 1.004 \\ Superior & 0.006 & 0.007 & 0.933 & 0.351 & 1.006 & 0.993 & 1.019 \\ Inferior & \(-\)0.002 & 0.005 & \(-\)0.522 & 0.601 & 0.998 & 0.989 & 1.007 \\ \hline
**Morpho-Geometric** & & & & & & \\ \hline Volume & 0.071 & 0.141 & 0.500 & 0.617 & 1.073 & 0.814 & 1.414 \\ Anterior area & \(-\)0.204 & 0.133 & \(-\)1.533 & 0.125 & 0.816 & 0.623 & 1.058 \\ Posterior area & 0.046 & 0.129 & 0.361 & 0.718 & 1.048 & 0.814 & 1.348 \\ \hline
**Intercepts** & & & & & & \\ \hline Ctrl vs. RETICS I & \(-\)16.769 & 0.017 & \(-\)98.428 & \(<\)0.001 & - & - & - \\ RETICS I vs. II & \(-\)13.926 & 0.396 & \(-\)35.205 & \(<\)0.001 & - & - & - \\ RETICS II vs. III & \(-\)12.270 & 0.522 & \(-\)23.530 & \(<\)0.001 & - & - & - \\ RETICS III vs. IV & \(-\)9.998 & 0.765 & \(-\)13.072 & \(<\)0.001 & - & - & - \\ \hline RETICS IV vs. V & \(-\)6.691 & 1.057 & \(-\)6.330 & \(<\)0.001 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the ordinal logistic regression model for the Ctrl and RETICS grade I-V patients. CDVA: Corrected Distance Visual Acuity; Q: asphericity; RMS, root mean square \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & **Predicted Value** & & \\ \hline
**True Value** & **Ctrl** & **I** & **II** & **III** & **IV** & **V** \\ \hline Ctrl & 55 & 7 & 0 & 0 & 0 & 0 \\ I & 12 & 26 & 4 & 2 & 0 & 0 \\ II & 2 & 9 & 6 & 3 & 1 & 0 \\ III & 0 & 3 & 3 & 6 & 3 & 0 \\ IV & 0 & 1 & 0 & 1 & 12 & 1 \\ V & 0 & 0 & 0 & 1 & 1 & 13 \\ Sensitivity & 0.887 & 0.591 & 0.333 & 0.400 & 0.800 & 0.867 \\ Specificity & 0.869 & 0.840 & 0.954 & 0.955 & 0.968 & 0.994 \\ Balanced accuracy & 0.878 & 0.715 & 0.643 & 0.677 & 0.884 & 0.930 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The ordinal logistic regression confusion matrix showing sensitivity, specificity and balanced accuracy for each group.