Background: Aggregated dynamic models for kidney transplant follow-up learning are generally described by AUROC, AUPRC, threshold precision, and feature importance. These characteristics are needed for model assessment, but they fail to answer the question of an operationally relevant deployment decision faced by the transplant network: how many patients will enter the review process per center, how many events will be captured, and what additional workload will be incurred through greater recall? Objective: The aim of this study was to find out if aggregate prognosis tables for annual death and graft-loss risk can be converted into a center-specific review ledger, which will reveal an operationally sound choice of an alerting threshold without creating a new predictive model and a new optimization procedure. Methods: Decision-Calibrated Domain Balancing (DCDB) and Multicenter Alert Stewardship Augmentation (MASA) were applied to the numeric tables generated by the STCS to convert them into review queue metrics. DCDB calculated prevalence-adjusted enrichment, longitudinal gain, workload, and domain saturation. MASA recovered the follow-up prevalence from recall, precision, and specificity; estimated flagged patients, captured events, and false positives; disaggregated alert counts by six center denominators; quantified threshold expansion; and summarized the effective predictor-domain breadth. Results: The deployment ledger included a longitudinal panel of model–outcome pairs (20 rows); an operating point panel (10 rows); a 60-row center–outcome–recall panel of alerts; and a domain panel (9 rows). LightGBM demonstrated the best graft-loss enrichment (ρ = 18.52); the death prediction model showed lower enrichment and faster workload growth. Follow-up prevalence recovery showed no threshold dependency, with the averages 2.82% for death and 1.51% for graft loss. A death recall rule of 0.50 will flag approximately 651 patients and capture around 68 events. A graft-loss recall rule of 0.60 will flag approximately 271 patients and capture 43 events; raising the graft-loss recall to 0.70 will bring the queue up to 516 patients capturing about 50 events. Zurich and Basel will generate the biggest review queues due to their large patient cohort denominator. Effective domain breadth was 2.51 for the pre-transplant inventory and 2.90 for the follow-up inventory, revealing that the nominal domain numbers overstate the explanatory power of the predictor domains. Conclusion: Aggregate prognosis tables can be converted into an actionable deployment case when the statistical performance is evaluated with regard to workload, center scale, and domain concentration. The study answered the question posed by providing a choice of a network-wide starting rule – graft-loss recall 0.60. Graft-loss recall 0.70 is a capacity-driven escalation, while broad death alerting calls for a stricter clinical filter prior to the deployment.