The "ABCD" framework for assessment: a task-oriented approach to reconcile multimodal diagnostic discordance

Pre-operative assessment of hiatal hernia (HH) often reveals discordance between endoscopy, barium swallow, and high-resolution manometry (HRM). These discrepancies can lead to diagnostic uncertainty and suboptimal surgical planning. We propose the "ABCD" framework to integrate these multimodal findings into a clinical-task-oriented decision process.

A comprehensive review of current diagnostic standards (Chicago 4.0, Lyon 2.0, AFS-LDF classification) and emerging technologies, including artificial intelligence (AI), was conducted to identify common points of diagnostic discordance and their clinical implications.

The ABCD framework categorizes assessment into four essential domains: anatomical confirmation (presence and type), Barrier phenotyping (EGJ integrity using Hill and AFS-LDF systems), Clinical consequences (reflux burden and symptoms), and Decision support (integrating data for surgical planning). This framework clarifies that discordance-such as a large hernia on imaging with a functional barrier on HRM-often reflects different dimensions of the EGJ rather than test failure. AI-assisted endoscopic grading may further improve reproducibility, although its clinical utility requires external validation.

The ABCD framework offers a structured, task-oriented approach for integrating multimodal HH assessment rather than a fully validated decision algorithm. By shifting focus from "the best test" to multidimensional clinical tasks, this framework may facilitate patient selection and perioperative decision-making, but its impact on clinical outcomes requires prospective validation.