Epistemic Fairness in Anastomotic Leak Prediction Through Outcome Design, Benchmark Governance, and Clinically Accountable Implementation Strategies

Abstract

Anastomotic leak prediction sits at an unusual intersection of surgical risk modeling, postoperative surveillance, and institutional decision making. Background conditions in this domain make the fairness question especially difficult. Anastomotic leak is infrequent, variably defined, often detected late, and documented through workflows that differ across hospitals and patient populations. These features mean that a model can seem technically competent while still allocating reassurance, scrutiny, and clinical attention unevenly. In this setting, fairness cannot be reduced to whether a classifier uses protected variables or whether one summary metric differs across subgroups. It is shaped earlier, at the moment when the outcome is defined, the cohort is assembled, and the benchmark is declared adequate for translation. This paper develops an account of fairness in anastomotic leak prediction by treating it as an epistemic property of the full research pipeline. The central claim is that fairness in this area depends on how knowledge is produced as much as on how predictions are computed. Outcome construction, data provenance, missingness, surveillance intensity, external validation strategy, workflow design, alert burden, and override behavior all influence whether a model distributes benefit and error in a clinically acceptable way. The discussion therefore shifts attention away from narrow metric debates toward benchmark governance, institutional heterogeneity, implementation accountability, and reproducible audit design. Rather than assuming that fairness can be certified by a single statistical test, the paper argues that equitable leak prediction requires a layered evidentiary standard. Under that standard, a model is considered fair only when its target is clinically coherent, its labels are defensible, its subgroup behavior is visible, its deployment pathway is specified, and its claims remain stable when moved beyond the setting in which it was first developed.