Evidence map›Paper›PMID 38202015›Full record

ArticleJournal of clinical medicine2023

Classification and Characterization of Traumatic Brain Injuries in the Northern Region of Sweden.

Beatrice M Magnusson, Lars-Owe D Koskinen

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 28% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
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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

2 authors at 1 institution in 1 country.

Beatrice M MagnussonDepartment of Surgery and Perioperative Sciences, Anaesthesiology and Intensive Care Medicine, Umeå University, 901 87 Umeå, Sweden.
Lars-Owe D KoskinenDepartment of Clinical Science, Neurosciences, Umeå University, 901 87 Umeå, Sweden.ORCID 0000-0003-3528-8502
Umeå University · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTraumatic brain injury (TBI) is a common cause of death and disability, the incidence of which in northern Sweden is not fully investigated. This study classifies and characterize epidemiological and demographic features of TBIs in a defined population in Umeå county, Sweden. Specifically, to evaluate frequencies of (1) intracranial lesions detected with computed tomography (CT), (2) need for emergency intervention, and (3) hospital admission, in minimal, mild, moderate, and severe TBI, respectively.

methodsThe data were gathered from 4057 TBI patients visiting our emergency room (ER) during a two-year period (2015-2016), of whom 56% were men and approximately 95% had minimal TBIs (Glasgow Coma Scale (GCS), score 15).

resultsOf all injuries, 97.8% were mild (GCS 14-15), 1.7% were moderate (GCS 9-13), and 0.5% were severe (GCS < 9). CT scans were performed on 46% of the patients, with 28% being hospitalized. A high annual TBI incidence of 1350 cases per 100,000 citizens was found. The mortality rate was 0.5% with the majority as expected in the elderly group (>80 years).

conclusionsMinimal TBIs were not as mild as previously reported, with a relatively high frequency of abnormal CT findings and a high mortality rate. No emergency intervention was required in patients in the GCS 13-15 group with normal CT scans. These findings have implications for clinical practice in the ER with the suggestion to include biomarkers to reduce unnecessary CT scans.

Indexed as

admissionCT scandemographicsepidemiologicalGlasgow Coma Scaleinterventionoutcomeprospectivetraumatic brain injury

Identifiers

PMID38202015
PMCPMC10780294
OpenAlexW4389982053

What OpenQuestion holds

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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 OpenQuestion graph.