## 5 Results
### SHAP-Based Explainability Analysis
(**b**) Both prediction sets. Figure 22: Comparison of SHAP values for single and both prediction sets under Split conformal prediction with post-hoc Isotonic calibration applied to the CatBoost model. (**a**) Single prediction set. (**b**) Both prediction sets. Figure 23: Comparison of SHAP values for single and both prediction sets under Split conformal prediction with post-hoc Spline calibration applied to the CatBoost model. (**a**) Single prediction set. (**b**) Both prediction sets. Figure 24: Comparison of SHAP values for single and both prediction sets under Split conformal prediction with post-hoc Inductive Venn-Abers calibration applied to the CatBoost model. (**a**) Single prediction set. (**b**) Both prediction sets. Figure 25: Comparison of SHAP values for single and both prediction sets under Split conformal prediction with post-hoc Platt calibration applied to the CatBoost model. (**a**) Single prediction set. (**b**) Both prediction sets. _Relevance of Explainability in Calibration-CP Frameworks._ The interplay between calibration and explainability carries profound implications for clinical deployment. Calibration methods, often perceived as purely statistical corrections, inherently reconfigure the model's internal reasoning--particularly in ambiguous cases. For instance, Isotonic Regression and Spline calibrations, by tethering uncertainty to clinically interpretable markers (e.g., lab anomalies or atypical diagnoses), enable clinicians to contextualize model hesitancy. A prediction flagged as uncertain due to elevated diagnostic-artstrup values, for example, could prompt targeted blood gas analysis, transforming uncertainty into a diagnostic cue. Conversely, methods like Venn-Abers or Platt Scaling, which decouple uncertainty from domain-specific features, risk producing opaque explanations that hinder root-cause analysis. This analysis underscores that calibration is not a neutral adjustment but a reinterpretive act that reshapes model transparency. In high-stakes settings like sepsis management, where clinician trust hinges on interpretability, the choice of calibration method must balance statistical rigor with explanatory coherence. A well-calibrated model that attributes uncertainty to non-clinical factors (e.g., hours_past_midnight) risks being dismissed as a \"black box\" whereas one linking ambiguity to plausible clinical variables (e.g., conflicting lab trends) fosters collaborative decision-making. _Synthesis and Clinical Implications._ The findings reveal a critical trade-off: parametric calibrations (Platt Scaling, Beta) enhance probability reliability but may dilute clinical interpretability, while non-parametric methods (Isotonic Regression, Spline) preserve feature relevance at the cost of increased computational complexity. For healthcare applications, Isotonic Regression emerges as a compelling compromise--its uncertainty explanations align with clinical workflows, enabling providers to reconcile model outputs with bedside observations. Spline calibration offers similar advantages but requires careful monitoring of its sensitivity to lab-result fluctuations. Ultimately, the integration of SHAP-based explainability with calibration-CP frameworks advances beyond mere technical validation; it bridges the gap between algorithmic outputs and clinical reasoning. By exposing how calibration reshapes feature attributions--and, by extension, model \"thinking\"--this approach empowers clinicians to audit uncertainty drivers, refine intervention protocols, and align predictive analytics with real-world care pathways. In doing so, it elevates PPM from a statistical exercise to a clinically embedded tool, where uncertainty is not a flaw but a diagnostically meaningful signal.