Investigating factors influencing injury severity in crashes involving vulnerable road users in Pakistan.

Muhammad Junaid, Chaozhe Jiang, Saleh Alotaibi, Tong Wang, Yahya Almarhab

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–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

1 citing paper in PubMed.

Consumer awareness, trust and cultural challenges in the use of child car seats in Iran: Current status and improvement strategies.PloS one · 2026
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.

Muhammad JunaidSchool of Transportation and Logistics, Southwest Jiaotong University (SWJTU), Chengdu, China.

Chaozhe JiangSchool of Transportation and Logistics, Southwest Jiaotong University (SWJTU), Chengdu, China. jiangchaozhe@swjtu.edu.cn.

Saleh AlotaibiCivil and Environmental Engineering Department, Faculty of Engineering-Rabigh Branch, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.

Tong WangTangshan Institute, Southwest Jiaotong University (SWJTU), Chengdu, China.

Yahya AlmarhabCenter of Excellence in Trauma and Accidents, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.

Funding

Tong Wang R113623H01099

6 · The paper itself

Abstract

Road traffic crashes claim around 1.19 million lives annually worldwide, with over half of the fatalities involving vulnerable road users (VRUs). While several studies have explored the risk factors associated with specific categories of VRUs in Pakistan, research focusing on VRUs collectively, considering all categories and their unique safety challenges, remains limited. This study aims to examine the influence of various risk factors on the severity of injuries resulting from crashes involving VRUs, using a three-year dataset (2021-2023). The study evaluated the effectiveness of six boosting-based ensemble machine learning classifiers across multiple evaluation metrics. The findings indicated that boosting with decision stumps outperformed extreme gradient boosting, light gradient boosting, histogram-based gradient boosting, categorical boosting, and adaptive boosting in terms of recall, F

Indexed as

Accidents, TrafficWounds and InjuriesAdolescentAdultAgedCOVID-19FemaleHumansMachine LearningMaleMiddle AgedPakistanRisk FactorsYoung AdultBoosting-based ensemble learningInjury severityPartial dependence plotsSHAP interaction strength plotVulnerable road users

Identifiers

PMID40897769

DOI10.1038/s41598-025-16477-5

PMCPMC12405482

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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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.