Evidence map›Paper›PMID 36675651›Full record

ArticleJournal of clinical medicine2023

The Value of the First Clinical Impression as Assessed by 18 Observations in Patients Presenting to the Emergency Department.

Thomas Tschoellitsch, Stefan Krummenacker, Martin W Dünser, Roland Stöger, Jens Meier

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.1field-weighted citation impact, top 15% of its field
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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Observational
  3. Review
  4. Article
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 at 1 institution in 1 country.

Thomas TschoellitschDepartment of Anesthesiology and Critical Care Medicine, Kepler University Hospital, Johannes Kepler University Linz, 4020 Linz, Austria.ORCID 0000-0002-3350-7532
Stefan KrummenackerKepler University Hospital, Johannes Kepler University Linz, 4020 Linz, Austria.
Martin W DünserDepartment of Anesthesiology and Critical Care Medicine, Kepler University Hospital, Johannes Kepler University Linz, 4020 Linz, Austria.
Roland StögerPraxis für Allgemein- und Familienmedizin, 4262 Leopoldschlag, Austria.
Jens MeierDepartment of Anesthesiology and Critical Care Medicine, Kepler University Hospital, Johannes Kepler University Linz, 4020 Linz, Austria.ORCID 0000-0002-8727-2257
Johannes Kepler University of Linz · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The first clinical impression of emergency patients conveys a myriad of information that has been incompletely elucidated. In this prospective, observational study, the value of the first clinical impression, assessed by 18 observations, to predict the need for timely medical attention, the need for hospital admission, and in-hospital mortality in 1506 adult patients presenting to the triage desk of an emergency department was determined. Machine learning models were used for statistical analysis. The first clinical impression could predict the need for timely medical attention [area under the receiver operating characteristic curve (AUC ROC), 0.73; p = 0.01] and hospital admission (AUC ROC, 0.8; p = 0.004), but not in-hospital mortality (AUC ROC, 0.72; p = 0.13). The five most important features informing the prediction models were age, ability to walk, admission by emergency medical services, lying on a stretcher, breathing pattern, and bringing a suitcase. The inability to walk at triage presentation was highly predictive of both the need for timely medical attention (p < 0.001) and the need for hospital admission (p < 0.001). In conclusion, the first clinical impression of emergency patients presenting to the triage desk can predict the need for timely medical attention and hospital admission. Important components of the first clinical impression were identified.

Indexed as

artificial intelligenceemergency departmentemergency medicinefirst clinical impressionhospital admissionmachine learningpredictiontriageurgency

Identifiers

PMID36675651
PMCPMC9862625
OpenAlexW4316663564

What OpenQuestion holds

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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.