Evidence map›Paper›PMID 42099926›Full record

ArticleFrontiers in digital health2026

An embodied cognition based model of medical experts' tacit knowledge: structure, hierarchies, and transformation.

Hailing Zhou, Xinyue Chang, Xiaoyang Zhou, Jin Shi, Zuojian Zhou, Sheng Zhong

Abstract read
In one paragraph

Article in Frontiers in digital health, 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

6 authors.

Hailing ZhouSchool of Information Management, Nanjing University, Nanjing, China.
Xinyue ChangSchool of Information Management, Nanjing University, Nanjing, China.
Xiaoyang ZhouDepartment of Blockchain R&D, China Mobile Zijin (Jiangsu) Innovation Research Institute, Nanjing, China.
Jin ShiSchool of Information Management, Nanjing University, Nanjing, China.
Zuojian ZhouSchool of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China.
Sheng ZhongNational Key Lab for Novel Software Technology, Nanjing University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Tacit knowledge plays a crucial role in clinical decision-making and medical innovation, particularly through experience-based and practice-oriented expertise. However, existing research has not yet provided a sufficiently integrated framework to explain how such knowledge is structured and transformed within medical practice. Methods: Grounded in embodied cognition theory, this study constructs a medical experts' tacit knowledge model encompassing four key elements of expert agent, context, thinking, and action. Building upon the layered perspective of the onion model, the study organizes tacit knowledge across three levels and explains its dynamic and bidirectional transformation. Results: The resulting framework integrates embodied experience, cognitive processes, and clinical practice into a coherent system. A case analysis of the acupuncture expert Wang Leting and his "Lao Shi Zhen" prescription is used to illustrate how the model operates in practice. Conclusion: The study provides a systematic perspective for understanding medical experts' tacit knowledge and offers theoretical insights for medical education, knowledge transmission, and clinical decision support.

Indexed as

contextualembodied cognitionmedical experttacit knowledgetransformation mechanism

Identifiers

PMID42099926
PMCPMC13144038

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.