Evidence map›Paper›PMID 41897687›Full record

ArticleDiagnostics (Basel, Switzerland)2026

Interpretable Machine Learning for Emergency Department Triage: Clinical Insights from 133,198 Patients Using the Korean Triage and Acuity Scale (KTAS).

MyoungJe Song, Jongsun Kim, Eun-Chul Jang, SoonChan Kwon

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 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

4 authors.

MyoungJe SongDepartment of Emergency Medicine, International St. Mary's Hospital, Catholic Kwandong University, Incheon 22711, Republic of Korea.ORCID 0000-0003-3091-2978
Jongsun KimDepartment of Emergency Medicine, International St. Mary's Hospital, Catholic Kwandong University, Incheon 22711, Republic of Korea.ORCID 0000-0002-8800-7412
Eun-Chul JangDepartment of Occupational and Environmental Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Republic of Korea.
SoonChan KwonDepartment of Occupational and Environmental Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Republic of Korea.ORCID 0000-0002-5782-6087

Funding

Catholic Kwandong University International St. Mary's Hospital IS25RIMI0063
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

decision-makingemergency department triageexplainable artificial intelligence (XAI)Korean triage and acuity scale (KTAS)machine learning

Identifiers

PMID41897687
PMCPMC13025175

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.