Evidence map›Paper›PMID 30583984›Full record

ArticleChinese journal of traumatology = Zhonghua chuang shang za zhi2019

Clinical characteristics of 1653 injured motorcyclists and factors that predict mortality from motorcycle crashes in Malaysia.

Henry Tan Chor Lip, Jih Huei Tan, Yuzaidi Mohamad, Affirul Chairil Ariffin, Rizal Imran, Tuan Nur' Azmah Tuan Mat

Open access · diamondAbstract read
In one paragraph

Article in Chinese journal of traumatology = Zhonghua chuang shang za zhi, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
3.3field-weighted citation impact, top 7% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.

  1. Pooled it
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  3. Observational
  4. Review
  5. Article
  6. Article
  7. Article
  8. Critical adjustments and trauma surgery trends in adaptation to COVID-19 pandemic in Malaysia.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2020
    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

6 authors at 2 institutions in 1 country.

Henry Tan Chor LipDepartment of General Surgery, Hospital Sultanah Aminah, Johor Bahru, Malaysia; Department of General Surgery, Faculty of Medicine, National University of Malaysia Medical Centre, Kuala Lumpur, Malaysia. Electronic address: relos1402@gmail.com.
Jih Huei TanDepartment of General Surgery, Hospital Sultanah Aminah, Johor Bahru, Malaysia; Department of General Surgery, Faculty of Medicine, National University of Malaysia Medical Centre, Kuala Lumpur, Malaysia.
Yuzaidi MohamadDepartment of General Surgery, Hospital Sultanah Aminah, Johor Bahru, Malaysia.
Affirul Chairil AriffinFaculty of Medicine and Allied Health, University Sains Islam Malaysia.
Rizal ImranDepartment of General Surgery, Hospital Sultanah Aminah, Johor Bahru, Malaysia.
Tuan Nur' Azmah Tuan MatDepartment of General Surgery, Hospital Sultanah Aminah, Johor Bahru, Malaysia.
Sultanah Aminah Hospital · MYUniversiti Sains Islam Malaysia · MY

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAmongst the ASEAN countries, Malaysia has the highest road fatality risk (>15 fatalities per 100 000 population) with 50% of these fatalities involving motorcyclist. This contributes greatly to ward admissions and poses a significant burden to the general surgery services. From mild rib fractures to severe intra-abdominal exsanguinations, the spectrum of cases managed by surgeons resulting from motorcycle accidents is extensive. The objective of this study is to report the clinical characteristics and identify predictors of death in motorcycle traumatic injuries from a Malaysian trauma surgery centre.

methodsThis is a prospective cross-sectional study of all injured motorcyclists and pillion riders that were admitted to Hospital Sultanah Aminah and treated by the trauma surgery team from May 2011 to February 2015. Only injured motorcyclists and pillion riders were included in this study. Patient demography and predictors leading to mortality were identified. Significant predictors on univariate analysis were further analysed with multivariate analysis.

resultsWe included 1653 patients with a mean age of (35 ± 16.17) years that were treated for traumatic injuries due to motorcycle accidents. The mortality rate was 8.6% (142) with equal amount of motorcycle riders (788) and pillion riders (865) that were injured. Amongst the injured were male predominant (1 537) and majority of ethnic groups were the Malays (897) and Chinese (350). Severity of injury was reflected with a mean Revised Trauma Score (RTS) of 7.31 ± 1.29, New Injury Severity Score (NISS) of 19.84 ± 13.84 and Trauma and Injury Severity Score (TRISS) of 0.91 ± 0.15. Univariate and multivariate analysis revealed that age≥35, lower GCS, head injuries, chest injuries, liver injuries, and small bowel injuries were significant predictors of motorcycle trauma related deaths with p < 0.05. Higher trauma severity represented by NISS, RTS and TRISS scores was also significant for death with p < 0.05.

conclusionAge, lower GCS, presence of head, chest, liver, small bowel injuries and higher severity on NISS, RTS and TRISS scores are predictive of death in patients involved with motorcycle accidents. This information is important for prognostic mortality risk prevention and counselling.

Indexed as

Accidents, TrafficAdultAge FactorsCross-Sectional StudiesFemaleForecastingHumansMalaysiaMaleMiddle AgedMotorcyclesTrauma Severity IndicesWounds and InjuriesYoung AdultAdultExsanguinationsMalaysiaMortalityMotorcyclesWounds and injuries

Identifiers

PMID30583984
PMCPMC6487457
OpenAlexW2902929002

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.