Evidence map›Paper›PMID 40682160›Full record

ArticleHereditas2025

Development and implementation of an etiology-based diagnostic framework for acute abdominal pain in emergency settings.

Hui Guo, Xu-Rui Li, Yun-Lei Du, Yang-Juan Jia, Hong-Ling Li, Qian Zhao, Yan-Peng Li, Jian-Guo Li

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Article in Hereditas, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Hui GuoDepartment of Emergency, Hebei General Hospital, No.348 West Heping Boulevard, Shijiazhuang, Hebei Province, 050051, China.
Xu-Rui LiDepartment of Emergency, Hebei General Hospital, No.348 West Heping Boulevard, Shijiazhuang, Hebei Province, 050051, China.
Yun-Lei DuDepartment of Emergency, Hebei General Hospital, No.348 West Heping Boulevard, Shijiazhuang, Hebei Province, 050051, China.
Yang-Juan JiaDepartment of Emergency, Hebei General Hospital, No.348 West Heping Boulevard, Shijiazhuang, Hebei Province, 050051, China.
Hong-Ling LiDepartment of Emergency, Hebei General Hospital, No.348 West Heping Boulevard, Shijiazhuang, Hebei Province, 050051, China.
Qian ZhaoDepartment of Emergency, Hebei General Hospital, No.348 West Heping Boulevard, Shijiazhuang, Hebei Province, 050051, China.
Yan-Peng LiDepartment of Emergency, Hebei General Hospital, No.348 West Heping Boulevard, Shijiazhuang, Hebei Province, 050051, China.
Jian-Guo LiDepartment of Emergency, Hebei General Hospital, No.348 West Heping Boulevard, Shijiazhuang, Hebei Province, 050051, China. lijg65@163.com.

Funding

Clinical medical talent training program funded by goverment in 2022 Hebei Province 2023 medical science research project No.20230282
6 · The paper itself

Abstract

backgroundDiagnostic checklists have been demonstrated to reduce errors in clinical reasoning. Building on previous validation studies, this research presents the development and clinical application of an etiology-based diagnostic framework for evaluating acute abdominal pain. The framework integrates a structured checklist of abdominal pain etiologies with a process-oriented diagnostic strategy, aiming to enhance diagnostic accuracy and clinical outcomes. This approach also serves as a potential model for the creation of diagnostic tools applicable to other symptom complexes encountered in emergency medicine.

methodsA cognitive task analysis (CTA) was conducted with participation from five emergency medicine experts employing a think-aloud methodology. The experts described their diagnostic reasoning processes and queried relevant clinical data to extract foundational diagnostic principles. Based on these findings, a checklist categorizing etiologies of abdominal pain was constructed, drawing from anatomical and diagnostic considerations. The clinical utility of the checklist was evaluated through its application to a representative complex case.

resultsThe diagnostic checklist was organized into five principal etiological categories: local organ disorders, diseases of adjacent organs, systemic diseases, psychogenic disorders, and gynecological conditions. Its implementation facilitated the accurate identification of atypical acute renal infarction in a diagnostically challenging case, enabling prompt clinical intervention.

conclusionsCTA provides a robust method for modeling expert diagnostic reasoning and supports the development of structured, etiology-based diagnostic tools. This framework enhances diagnostic precision for individuals presenting with acute abdominal pain in emergency settings and may inform the development of similar tools for other clinical presentations.

Indexed as

Abdominal PainChecklistFemaleHumansMaleAbdominal painAcute renal infarctionChecklistClinical decisionCognitive loadCognitive task analysis

Identifiers

PMID40682160
PMCPMC12273047

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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.