External validation in multi-center cohorts represents the next critical step for clinical implementation47. Integration of additional data sources, such as continuous vital sign monitoring or novel b

The gap

Consensus across the literature

Supporting evidence — 3 representative gaps

• Building and validating machine learning models to predict appendiceal perforation during conservative treatment of fecalith-associated appendicitis: a 20-algorithm multicenter retrospective analysis (2026) · doi

  External validation in multi-center cohorts represents the next critical step for clinical implementation47. Integration of additional data sources, such as continuous vital sign monitoring or novel biomarkers, may further enhance predictive accuracy49. Real-time implementation studies will be essential to evaluate clinical impact, workflow integration, and cost-effectiveness. Regarding deployment format, we envisage the Gradient Boosting model being made available as a web-based risk calculator (planned deployment via a dedicated URL) allowing clinicians to enter the eight input variables at the bedside. Prospective real-time implementation studies will be essential to evaluate clinical impact, workflow integration, and cost-effectiveness50. Development of a simplified scoring chart derived from the model, while sacrificing some predictive accuracy, may improve adoption in settings without electronic clinical decision support51. Investigation of optimal intervention strategies for high-risk patients identified by these models represents an important clinical question. Whether enhanced monitoring, alternative antibiotic regimens, or earlier surgical intervention improves outcomes requires prospective evaluation52.

  Keywords: clinical implementation integration represents monitoring predictive accuracy real time essential evaluate impact workflow cost effectiveness
• Unified comparison of machine learning paradigms for blood transfusion prediction in pediatric congenital heart surgery (2026) · doi

  Several limitations should be acknowl- edged. First, this is a single-center retro- spective study. We employed a stratified random split (80/20) rather than temporal splitting to avoid confounders from evolving surgical techniques over the study period; the comparability of training and test sets was confirmed in Table S1, and training set performance is pro- vided in Figure S1. However, external validation using indepen- dent multi-center cohorts remains essential for confirming generalizability. Second, the reliance on preoperative static vari- ables to predict a dynamic intraoperative process is an inherent limitation, as actual transfusion decisions are influenced by real- time factors such as surgical blood loss and point-of-care testing results.24,25 Future research should explore the integration of real-time intraoperative data to develop dynamic prediction models. Third, probability calibration analysis showed good to excellent Brier scores (RBC: 0.103; plasma: 0.209; platelet: 0.016; Figure S2), though in clinical deployment, the operating threshold should be adjusted to prioritize sensitivity given the greater harm of under-preparation. Future work should also incorporate formal model uncertainty quantification and post hoc probability calibration techniques. wastage while ensuring adequate supply for patients predicted to require transfusion. Decision curve analysis confirmed positive net clinical benefit across broad threshold ranges for all three recommended models (Figure S3). This framework directly addresses the methodological gaps identified in recent reviews,6 which emphasized the need for standardized reporting of data preprocessing and model validation procedures. The modular design allows adaptation to different institutional con- texts while maintaining methodological rigor, facilitating external validation and multi-center collaboration essential for clinical translation. In summary, this study’s primary contribution is the develop- ment of a dual-MAE composite metric that enables fair compari- son across fundamentally different prediction paradigms (direct regression, two-stage, and multi-class classification) for blood transfusion prediction. The benchmark comparison demon- strates that the two-stage approach achieves 5.9% (RBC) and 6.1% (plasma) improvement over direct regression, and 10 iScience 29, 116181, June 19, 2026 iSciencellArticle RESOURCE AVAILABILITY

  Keywords: center validation multi transfusion prediction clinical surgical techniques training confirmed external essential dynamic intraoperative real
• Development and internal validation of a prediction model for hypoxic hepatitis after coronary artery bypass grafting with cardiopulmonary bypass (2026) · doi

  Several limitations deserve emphasis. First, the study was retrospective and single-center, so differences in patient mix, surgical practice, CPB management, laboratory monitoring, and postoperative care may limit transportability. External validation is essential before broader implementation. Second, the outcome definition was operational and based primarily on severe postoperative aminotransferase elevation after exclusion of alternative causes during data extraction. A prospective blinded adjudication committee and inter-rater reliability assessment were not available, so misclassification bias remains possible. Third, incorporation bias cannot be fully excluded because CPB duration and lactate are conceptually related to perioperative hypoperfusion, even though they were not used as formal components of the biochemical outcome threshold. This overlap may inflate associations and internal performance. Fourth, there were only 57 events, and no formal prospective sample-size calculation was performed before data collection. The event count constrained model complexity and makes machine-learning analyses particularly vulnerable to overfitting. Fifth, despite low missingness and use of multiple imputation, the missing-data mechanism cannot be proven in a retrospective dataset. Sixth, the Youden-index threshold was internally derived and should be considered exploratory rather than a clinical decision cutoff. Finally, only in-hospital outcomes were available, and the long- term prognostic impact of the HH-compatible outcome could not be assessed.

  Keywords: outcome retrospective postoperative prospective available bias cannot formal threshold several limitations deserve emphasis first single