## 5 Results
### Inspection of Certain Predictions  
When the CP framework produces a single-label outcome, it designates a high-confidence prediction for a particular instance. Although most calibration strategies yield broadly similar proportions of such one-label sets, there are notable differences in how accurately each method classifies the positive and negative classes within this \"certain\" subset. The Table 6 reveals consistent performance patterns: Isotonic Regression and Spline typically offer elevated recall or precision in these confident instances, while Platt Scaling provides unusually strong specificity. Beta often strikes a middle-ground, balancing both recall and precision without dominating in any single metric. Figures 16-20 corroborate these observations and offer a deeper comparative lens. Figure 16 (Recall for certain data) shows Isotonic Regression achieving a mean of 0.5525, significantly higher than Platt Scaling at 0.3594 (\(\chi^{2}_{\text{[PERSON]}}=207.1656\), \(p=8.3203\times 10^{-43}\)). This indicates that when the Isotonic-based SCP method is sufficiently confident to assign a single label, it more reliably flags actual readmissions than Platt Scaling. By contrast, Spline emerges with the highest precision (0.9671 in Figure 17), suggesting fewer false positives among confidently predicted patients, an attribute that may reduce undue resource allocation to non-relapsing cases. The Friedman test further confirms that Platt Scaling, Beta, and Uncalibrated CatBoost differ from Isotonic Regression or Spline in precision ranks (\(\chi^{2}_{\text{Friedman}}=88.94143\), \(p=1.1211\times 10^{-17}\)). Similar distinctions manifest in specificity and MCC (Figures 18 and 19). Platt Scaling delivers near-perfect specificity (0.9985) for certain predictions, whereas Beta and Uncalibrated CatBoost range closer to 0.9974-0.9984, with Spline also above 0.9980. Higher specificity reduces false alarms but can curb recall by excluding borderline positive patients. The MCC values reveal a parallel story: Spline peaks at 0.7002, surpassing both Beta and Isotonic Regression (0.6859 and 0.6943, respectively), attesting to its consistent balance of true positives and true negatives within single-label decisions. The Friedman statistic (\(\chi^{2}_{\text{Friedman}}=167.216,p=2.8643\times 10^{-34}\)) and subsequent pairwise comparisons confirm Spline's significant advantage over Platt Scaling's more conservative approach (0.4814 MCC). _Clinical Relevance and Technical Considerations._ From a clinical perspective, these findings highlight critical trade-offs in patient classification strategies. The recall and precision distributions across calibration methods directly influence patient triage decisions, particularly in high-stakes medical scenarios such as early sepsis detection, post-operative monitoring, and hospital readmission risk assessment. The model's ability to confidently predict a single-label classification directly impacts clinical workflows and intervention timing. Isotonic Regression's superior recall implies that more high-risk patients would be flagged with a certain positive classification, ensuring that at-risk individuals receive necessary monitoring or intervention. This attribute is particularly crucial in conditions where early warning signs are subtle yet predictive, such as sepsis onset or cardiac decompensation. However, high recall at the expense of specificity may increase unnecessary hospital admissions or treatments, leading to higher resource utilization and potential patient burden from false positives. Conversely, Platt Scaling and Spline, with their higher specificity and precision, minimize unnecessary interventions, favoring a more conservative approach. This calibration choice is preferable in cases where false positives carry substantial costs, such as invasive procedures or intensive resource allocation (e.g., ICU admission).