Evidence map›Paper›PMID 42498962›Full record

ArticleScandinavian journal of trauma, resuscitation and emergency medicine2026

Evaluating performance and potential clinical benefit of the Swedish On Scene Injury Severity Prediction (OSISP) model for prehospital field triage on Norwegian trauma data.

Anna Bakidou, Eva-Corina Caragounis, Magnus Andersson Hagiwara, Olav Røise, Anders Jonsson, Bengt Arne Sjöqvist, Stefan Candefjord

Abstract read
In one paragraph

Article in Scandinavian journal of trauma, resuscitation and emergency medicine, 2026. 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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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

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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

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5 · Who and what money

Authors and funding

7 authors.

Anna BakidouDepartment of Electrical Engineering, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden. bakidou@chalmers.se.ORCID http://orcid.org/0000-0002-4288-8146
Eva-Corina CaragounisDepartment of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, 413 45, Sweden.ORCID http://orcid.org/0000-0001-5637-2637
Magnus Andersson HagiwaraPreHospen - Centre for Prehospital Research, Faculty of Caring Science, Work Life and Social Welfare, University of Borås, Borås, SE-501 90, Sweden.ORCID http://orcid.org/0000-0002-6888-9323
Olav RøiseResearch and Development Department, Division of Orthopedics, P.O. Box 4950, Nydalen, Oslo, Norway.ORCID http://orcid.org/0000-0003-4931-7001
Anders JonssonPreHospen - Centre for Prehospital Research, Faculty of Caring Science, Work Life and Social Welfare, University of Borås, Borås, SE-501 90, Sweden.ORCID http://orcid.org/0000-0001-7928-7021
Bengt Arne SjöqvistDepartment of Electrical Engineering, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden.ORCID http://orcid.org/0000-0002-6564-737X
Stefan CandefjordDepartment of Electrical Engineering, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden.ORCID http://orcid.org/0000-0001-7942-2190

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRule-based field triage protocols used by Emergency Medicine Service (EMS) personnel for assessment and prioritization of trauma patients achieve suboptimal field triage performance. An On Scene Injury Severity Prediction (OSISP) model was previously developed on Swedish trauma data with indications to improve field triage performance. This study aims to apply OSISP on external Norwegian data to assess its performance and clinical impact on unseen data.

methodsAdult trauma incidents involving EMS resources at the scene of incident were included. An eXtreme Gradient Boosting OSISP model was developed on Swedish trauma registry (SweTrau) data for 2013-2020 and trained to predict severely injured patients defined as new injury severity score (NISS) > 15. OSISP was evaluated on Norwegian trauma registry (NTR) data for 2017-2022. The model performance on NTR data was evaluated using overall performance measures for discrimination and calibration (receiver operating characteristic curve, precision-recall curve, area under these curves, Brier score, calibration curve, calibration slope, calibration in the large). OSISP's clinical impact was assessed by evaluating its under- and overtriage rates and comparing to current clinical outcome and mortality.

resultsThe raw data from SweTrau and NTR had 75,602 and 76,529 registrations, with 47,357 and 29,709 remaining after applying eligibility criteria. In the included data, there were 16.9% and 25.2% patients with NISS > 15 in SweTrau and NTR, respectively. The OSISP predictors represented age, sex, details of the patient's condition, incident and injury details, vital signs, and administrative details. OSISP applied on NTR data yielded an overall performance of AUC

conclusionsOSISP's overall performance was successfully validated and a potential capability to improve triage and decrease mortality was observed, also for elderly and patients from the northern region, groups often missed with current triage tools. REGISTRATION: Not applicable.

trial registrationNot applicable.

Indexed as

Emergency Medical ServicesTriageWounds and InjuriesAdultFemaleHumansInjury Severity ScoreMaleMiddle AgedNorwayRegistriesSwedenArtificial Intelligence (AI)Diagnostic modellingExternal validationMachine Learning (ML)Norwegian Trauma Registry (NTR)On Scene Injury Severity Prediction (OSISP)Prehospital careSwedish Trauma Registry (SweTrau)Trauma

Identifiers

PMID42498962
PMCPMC13401289

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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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 Socratic graph.