## 6 Discussion
Latency is minimal at the point of care, as all computationally intensive training and calibration steps are performed offline, leaving only rapid, low-latency calculations for real-time inference. For interpretability, our SHAP-based analysis provides the crucial backend for a user-friendly clinical interface. By showing that calibration can align feature attributions with clinically relevant factors, we lay the groundwork for explanations that are not just available but also meaningful to a clinician. Most importantly, the framework is architected to build trust. It combines reliable, calibrated probabilities with the honest uncertainty of Conformal Prediction. When the model is uncertain about a case, it does not provide a single, potentially misleading prediction. Instead, it flags the case for human review by producing an ambiguous prediction set. This \"knows what it doesn'tknow\" capability is fundamental for enabling safe, human-in-the-loop decision-making and fostering clinician confidence in the system. Limitations as Catalysts for InnovationWe must acknowledge the limitations of this study to appropriately contextualize our findings. The analysis is based on a single, modestly-sized dataset from one hospital. This naturally limits the direct generalizability of the trained predictive model to other clinical settings, and we make no claim that this specific model is ready for deployment. However, the primary contribution of this work is the methodological framework itself. The dataset, with its characteristic clinical complexity and class imbalance, serves as a realistic testbed to rigorously evaluate the interplay between prediction, calibration, and uncertainty quantification. To ensure the robustness of our methodological insights, we employed an extensive bootstrapping procedure. This provides strong evidence that our findings--such as the observed trade-offs between different calibration methods and their downstream effects on conformal prediction and explainability--are stable and not an artifact of a single data split. Therefore, we propose our framework as a transferable blueprint for developing and validating trustworthy predictive models. A crucial and necessary direction for future work is to apply and validate this entire framework on larger, multi-center, and contemporary datasets to confirm the generalizability of these methodological findings across diverse patient populations and evolving clinical practices. The study's focus on a single clinical context (sepsis) and static datasets invites exploration into dynamic, evolving care environments. Future work must test whether these findings generalize to settings with concept drift (e.g., emerging pathogens) or heterogeneous patient populations. Additionally, the computational costs of non-parametric calibration methods pose barriers to real-time deployment--a gap that hybrid approaches (e.g., adaptive Spline calibration) could bridge. Furthermore, while our study used aggregated AUC metrics to select the best base classifier, we acknowledge that a more granular analysis of the full ROC and Precision-Recall curves, particularly in the low false-positive rate regime, would offer deeper insights into model trade-offs for clinical deployment. Such a detailed investigation represents a valuable direction for future work. Finally, the ethical dimension of uncertainty attribution warrants scrutiny: if models attribute ambiguity to socioeconomic features (e.g., insurance status) rather than clinical factors, they risk exacerbating care disparities.