Evidence map›Paper›PMID 35094688›Full record

ArticleBMC medical research methodology2022

Sex-specific analysis of traumatic brain injury events: applying computational and data visualization techniques to inform prevention and management.

Tatyana Mollayeva, Andrew Tran, Vincy Chan, Angela Colantonio, Michael D Escobar

Open access · goldAbstract read
In one paragraph

Article in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
2.3field-weighted citation impact, top 13% 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

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

  1. Pooled it
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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

5 authors at 5 institutions in 1 country.

Tatyana MollayevaKITE-Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada. tatyana.mollayeva@utoronto.ca.
Andrew TranAcquired Brain Injury Research Lab, University of Toronto, Toronto, Ontario, Canada.
Vincy ChanKITE-Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada.
Angela ColantonioKITE-Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada.
Michael D EscobarDalla Lana School of Public Health, University of Toronto, Toronto, Canada, Ontario.
Ontario Brain Institute · CAPublic Health Ontario · CAToronto Rehabilitation Institute · CAUniversity Health Network · CAUniversity of Toronto · CA

Funding

Machine learning to inform health services and policy for traumatic brain injuryR01NS117921 · NINDS · UNIVERSITY OF TORONTO · PI COLANTONIO, ANGELA, ESCOBAR, MICHAEL D · 2020 to 2021
$374k
NINDS NIH HHS R01 NS117921
6 · The paper itself

Abstract

backgroundThe interplay of host, agent, and environment implicated in traumatic brain injury (TBI) events is difficult to account for in hypothesis-driven research. Data-driven analysis of injury data can enable insight into injury events in novel ways. This research dissected complex and multidimensional data at the time of the TBI event by exploiting data mining and information visualization methods.

methodsWe drew upon population-based decade-long health administrative data collected through the routine operation of the publicly funded health system in Ontario, Canada. We applied a computational approach to categorize health records of 235,003 patients with TBI versus the same number of reference patients without TBI, individually matched based on sex, age, place of residence, and neighbourhood income quantile. We adopted the basic concepts of the Haddon Matrix (host, agent, environment) to organize emerging factors significantly related to TBI versus non-TBI events. To explore sex differences, the data of male and female patients with TBI were plotted on heatmaps and clustered using hierarchical clustering algorithms.

resultsBased on detected similarities, the computational technique yielded 34 factors on which individual TBI-event codes were loaded, allowing observation of a set of definable patterns within the host, the agent, and the environment. Differences in the patterns of host, agent and environment were found between male and female patients with TBI, which are currently not identified based on data from injury surveillance databases. The results were internally validated.

conclusionsThe study outlines novel areas for research relevant to TBI and offers insight into how computational and visual techniques can be applied to advance the understanding of TBI event. Results highlight unique aspects of sex differences of the host and agent at the injury event, as well as differences in exposure to adverse social and environmental circumstances, which can be a function of gender, aiding in future studies of injury prevention and gender-transformative care.

Indexed as

Brain Injuries, TraumaticData VisualizationData MiningFemaleHumansMaleOntarioData-driven researchEnvironmentHealth analyticsHuman factorsPhysical force agentSexThe Haddon matrix

Identifiers

PMID35094688
PMCPMC8802441
OpenAlexW4210336058

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.