Evidence map›Paper›PMID 41131933›Full record

ArticleJournal of Yeungnam medical science2025

Effect of reducing urban road speed limits on motorcyclist traffic accidents: a before-and-after study using the National Emergency Department Information System of South Korea.

Seunghyun Baek, Jung Ho Kim

Abstract read
In one paragraph

Article in Journal of Yeungnam medical science, 2025. 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
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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

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

2 authors.

Seunghyun BaekDepartment of Emergency Medicine, Yeungnam University College of Medicine, Daegu, Korea.
Jung Ho KimDepartment of Emergency Medicine, Yeungnam University College of Medicine, Daegu, Korea.

Funding

Chunma Medical Research Foundation
6 · The paper itself

Abstract

backgroundMotorcycles are widely used in various transportation sectors, but riders are directly exposed to external risks. Consequently, motorcyclists are more vulnerable to severe injuries. Road speed limits serve as key policy interventions to mitigate this risk. This study aims to evaluate the effects of reducing urban road speed limits on motorcyclist traffic accidents.

methodsIn this study, National Emergency Department Information System data from the seven largest cities in South Korea were analyzed by comparing a pre-implementation (April 17, 2018 to April 16, 2019) and post-implementation (April 17, 2021 to April 16, 2022) phase. The Pearson chi-square test was used. Additionally, univariable and multivariable logistic regression analyses were performed to assess the effects of the policy on emergency operations, intensive care unit (ICU) admission, and clinical outcomes. Statistical significance was set at p<0.05.

resultsThe number of patients decreased from 16,124 to 13,201, along with a reduction in emergency surgeries (n=61) and ICU admissions (n=184); however, unfavorable outcomes increased (n=9). The risk of emergency surgery (adjusted odds ratio [aOR], 1.093; 95% confidence interval [CI], 0.935-1.277) was not statistically significant. However, the risk of ICU admission (aOR, 1.147; 95% CI, 1.015-1.296) and unfavorable outcomes (aOR, 1.502; 95% CI, 1.052-2.145) increased significantly in the post-implementation period.

conclusionAlthough the number of patients significantly decreased, there was no corresponding improvement in clinical outcomes. Instead of abolishing the policy, revising it would be a more appropriate approach. Therefore, additional public interventions and educational programs are required.

Indexed as

MotorcyclistsOutcomePublic policySpeedTraffic accident

Identifiers

PMID41131933
PMCPMC12628830

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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.