Evidence map›Paper›PMID 42487936›Full record

ReviewFrontiers in medicine2026

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

Wubulikasimu Mijiti, Shadike Apaer, Xu Han, Qiang Dong, Xinling Cao

Abstract readReview
In one paragraph

Review in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Wubulikasimu Mijiti *Department of Hepatic Laparoscopic Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
Shadike Apaer *Department of Hepatic Laparoscopic Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
Xu HanDepartment of Hepatic Laparoscopic Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
Qiang DongDepartment of Hepatic Laparoscopic Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
Xinling CaoDepartment of Hepatic Laparoscopic Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: 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. Methods: 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. Results: 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. Conclusion: 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.

Indexed as

artificial intelligenceesophagogastric junctionhiatal herniamultimodal imagingsurgical decision-making

Identifiers

PMID42487936
PMCPMC13388164

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.