Evidence map›Paper›PMID 39333599›Full record

ArticleScientific reports2024

Emergency department triaging using ChatGPT based on emergency severity index principles: a cross-sectional study.

Cansu Colakca, Mehmet Ergın, Habibe Selmin Ozensoy, Alp Sener, Selahattin Guru, Ayhan Ozhasenekler

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 2 pooled it
–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

26 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Beyond awareness: Defining AI readiness in Emergency Medicine across health systems.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2026
    Article
  4. Subgroup Differences in Agreement Between an Algorithm Guided Large Language Model and Routine Emergency Department Triage.Medical science monitor : international medical journal of experimental and clinical research · 2026
    Article
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  15. Safety and accuracy of AI in triaging patients in the emergency department.International journal of emergency medicine · 2025
    Article
  16. Observational
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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.

Cansu ColakcaDepartment of Emergency Medicine, Ankara Bilkent City Hospital, Ankara, Turkey.ORCID 0009-0000-8585-0415
Mehmet ErgınDepartment of Emergency Medicine, Ankara Bilkent City Hospital, Ankara, Turkey.ORCID 0000-0002-9420-6514
Habibe Selmin OzensoyDepartment of Emergency Medicine, Ankara Bilkent City Hospital, Ankara, Turkey. habibeozensoy@gmail.com.ORCID 0000-0001-9261-2669
Alp SenerDepartment of Emergency Medicine, Ankara Bilkent City Hospital, Ankara, Turkey.ORCID 0000-0002-0583-2936
Selahattin GuruDepartment of Emergency Medicine, Ankara Bilkent City Hospital, Ankara, Turkey.ORCID 0000-0002-0299-1691
Ayhan OzhaseneklerDepartment of Emergency Medicine, Ankara Bilkent City Hospital, Ankara, Turkey.ORCID 0000-0002-2707-4099

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Erroneous and delayed triage in an increasingly crowded emergency department (ED). ChatGPT is an artificial intelligence model developed by OpenAI

Indexed as

Emergency Service, HospitalSeverity of Illness IndexTriageAdultAgedAged, 80 and overArtificial IntelligenceCross-Sectional StudiesFemaleHumansMaleMiddle AgedNatural Language ProcessingArtificial intelligenceEmergency departmentEmergency severity indexTriage

Identifiers

PMID39333599
PMCPMC11436771

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.