## 3 Results  
Analyzing the Correlation Between Observed Cobb Angles and Predictive Probabilities of Soliosis Using the BiomedCLIP Model  
The analysis utilizing the BiomedCLIP model to predict the likelihood of scoliosis from radiographic images yielded probabilities ranging from 0.097 to 0.818, demonstrating a considerable variation in the model's certainty across the spectrum of scoliosis severities and types. A correlation analysis was conducted to evaluate the relationship between the observed Cobb angles and the predictive probabilities generated by the model. Based on the results in Table 2, in the overall sample of 262 images, a moderate correlation (r = 0.37) was observed, CI 95%: 0.26-0.47, which was statistically significant (t = 6.39, \(p\) < 0.001). This suggests that, generally, the model's predictions align moderately well with the clinically observed Cobb angles across all cases. However, when the data were stratified by the stage of scoliosis, the strength of correlation varied. In the mild stage group (\(n\) = 43), the correlation was weaker (r = 0.24) and not statistically significant (\(p\) = 0.118), indicating a lower predictive accuracy of the model in this subgroup. The moderate stage group (\(n\) = 85) also showed a weaker, non-significant correlation (r = 0.19, \(p\) = 0.077). The severe stage group (\(n\) = 134), however, displayed a slight yet statistically significant correlation (r = 0.18, \(p\) = 0.041), suggesting some level of predictive reliability in more advanced stages of scoliosis. The analysis within scoliosis types revealed that the model performed better in predicting the occurrence of scoliosis in both single (r = 0.36, \(p\) < 0.001) and double (r = 0.44, \(p\) < 0.001) curvature types, with both correlations being statistically significant and stronger compared to the stage-based groups. This indicates that the model is more reliable in distinguishing the type of scoliosis rather than its severity stage. The results of the correlation analyses were depicted through scatter plots with linear regression lines, as illustrated in Figure 3.  
The varying degrees of correlation across different stages and types suggest that the model's predictive capabilities may benefit from further refinement, particularly for early-stage scoliosis detection where its current predictive certainty is lower (Table 3). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Characteristic** & **n\({}_{\text{obs}}\)** & **r** & **CI 95\%** & **t** & \(p\)** \\ \hline Overall sample & 262 & 0.37 & 0.26–0.47 & 6.39 & \textless{}0.001 \\ \multicolumn{5}{c}{Within scoliosis severity forms} \\ Mild & 43 & 0.24 & \(-\)0.06–0.51 & 1.60 & 0.118 \\ Moderate & 85 & 0.19 & \(-\)0.02–0.39 & 1.79 & 0.077 \\ Severe & 134 & 0.18 & 0.01–0.34 & 2.07 & 0.041 \\ \multicolumn{5}{c}{Within scoliosis types} \\ Single & 149 & 0.36 & 0.21–0.49 & 4.61 & \textless{}0.001 \\ Double & 113 & 0.44 & 0.28–0.58 & 5.23 & \textless{}0.001 \\ \hline \hline \end{tabular} Note: n\({}_{\text{obs}}\)—number of observations; \(r\)—[PERSON]’s correlation coefficient; CI 95%—confidence interval 95%; t—statistic of \(t\)-test; \(p\)—\(p\)-value of statistical test.  
\end{table} 
Table 3: Correlation analysis between observed Cobb angles and predictive probabilities of scoliosis using the BiomedCLIP model, stratified by scoliosis stage and type. Figure 3: Scatterplots illustrating the relationship between observed Cobb angles and predicted probabilities of scoliosis incidence as determined by the BiomedCLIP model, with fitted lines for the overall sample (**upper**), scoliosis stage (**middle**), and scoliosis type (**lower**). Estimating Optimal Cutpoints and Performance Metrics for Predictions of Soliosis Severity and Types by BiomedCLIP Model  
Analogous to the findings presented in Section 3.1, the results of the BiomedCLIP model were quantified on a numeric scale ranging from 0 to 1, representing the probability of event incidence.