## 3 Results
Descriptive statistics detailing the probabilities for events studied independently, including the occurrence of mild, moderate, and severe scoliosis, as well as single and double scoliosis types, are displayed in Table 4.  
Based in the results in Table 4, the BiomedCLIP model demonstrates differentiated predictive capabilities across different severity levels and types of scoliosis. The higher median probabilities and narrower interquartile ranges for single-curve scoliosis and moderate severity suggest that the model is more certain and consistent in these predictions. In contrast, severe scoliosis and double-curve scoliosis exhibit broader ranges of probabilities, indicating higher variability and less certainty in these predictions. This variability could stem from the inherent complexities and diverse manifestations of severe and double-curve scoliosis, which may challenge the model's predictive accuracy. The data presented in Table 5 provide a comprehensive analysis of the BiomedCLIP model's capability to predict scoliosis severity stages and types using estimated optimal cutpoints and associated performance metrics. For the scoliosis severity forms, the model demonstrates varying degrees of effectiveness. In predicting mild scoliosis, the model achieves a moderate accuracy = 0.64, with a sensitivity = 0.79 and specificity = 0.61, supported by an AUC = 0.75, suggesting reasonable discriminative ability though with some limitations in specificity. The moderate scoliosis prediction shows a slightly better performance with an accuracy = 0.70, sensitivity = 0.74, and specificity = 0.68, which indicates a balanced ability in identifying true positives and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Characteristic** & **Mdn** & **Q1** & **Q3** & **Min** & **Max** \\ \hline \multicolumn{6}{c}{Scoliosis severity} \\ Mild & 0.10 & 0.07 & 0.13 & 0.01 & 0.22 \\ Moderate & 0.46 & 0.35 & 0.59 & 0.10 & 0.72 \\ Severe & 0.44 & 0.29 & 0.58 & 0.12 & 0.88 \\ \multicolumn{6}{c}{Scoliosis type} \\ Single curve & 0.60 & 0.53 & 0.64 & 0.32 & 0.77 \\ Double curve & 0.40 & 0.36 & 0.47 & 0.23 & 0.68 \\ \hline \hline \end{tabular} Note: N—sample size; n—group size; Mn—median; Q1—first quartile (25%); Q3—third quartile (75%), Min—minimum value; Max—maximum value.  
\end{table} 
Table 4: Distribution of the probabilities of the specified event incidence by BiomedCLIP model, \(N=262\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Positive** & **Optimal** & **Accuracy** & **Sens.** & **Spec.** & **AUC** \\ \hline \multicolumn{6}{c}{Within scoliosis severity groups} \\ Mild & yes & \(\geq\)0.10 & 0.64 & 0.79 & 0.61 & 0.75 \\ Moderate & yes & \(\geq\)0.48 & 0.70 & 0.74 & 0.68 & 0.74 \\ Severe & yes & \(>\)0.42 & 0.80 & 0.84 & 0.77 & 0.87 \\ \multicolumn{6}{c}{Within scoliosis types} \\ Single curve & yes & \(\geq\)0.53 & 0.53 & 0.35 & 0.78 & 0.53 \\ Double curve & yes & \(\geq\)0.37 & 0.53 & 0.78 & 0.35 & 0.53 \\ \hline \hline \end{tabular} Note: Sens.—sensitivity; Spec.—specificity.  
\end{table} 
Table 5: Estimated optimal cutpoints and corresponding performance metrics for the prediction of scoliosis severity stages and types via the BiomedCLIP model. true negatives, mirrored by an AUC = 0.74. Predictions for severe scoliosis deliver the highest accuracy = 0.80, accompanied by an impressive sensitivity = 0.84 and specificity = 0.77, reflected in the highest AUC = 0.87 among the group. This indicates a strong predictive capability of the model in identifying severe cases, which is crucial for prioritizing medical interventions. This could be attributed to more pronounced physiological markers or imaging characteristics that are easier to detect and differentiate at severe stages. The performance of the model, as visualized through ROC curves for specific scoliosis severity stages, is detailed in Figure 4. The lower boundary of the 95% confidence interval for each curve was distinctly above 0.50, indicating a clear superiority in the model's discriminative ability compared to mere guessing.