Evidence map›Paper›PMID 29854144›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017

A Configurational Analysis of Risk Patterns for Predicting the Outcome After Traumatic Brain Injury.

Niku Gorji, Zsolt Zador, Simon Poon

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. 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
–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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Niku GorjiSchool of Information Technologies, The University of Sydney, NSW, Australia.
Zsolt ZadorDepartment of Neurosurgery, Salford Royal Foundation Trust, Salford, Greater Manchester, United Kingdom.
Simon PoonSchool of Information Technologies, The University of Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Exploring relationships between admission variables and outcome using regression models has been the focus of Traumatic Brain Injury (TBI) research. Although practical and well established, these approaches do not evaluate interactions between predictors. We therefore applied a set-theoretic logical analysis to the Corticosteroid Randomization after Significant Head Injury (CRASH) trial database. Complete data analysis of 6945 patients demonstrated 9 different configurations of admission variables were sufficient for favorable outcome in 87.5% of all cases and explained 57% of favorable outcomes (moderate disability or good outcome). We also evaluated the contrasting configurations for unfavorable versus favorable outcome. Results are largely in line with findings of previous studies however the influence of age fell behind GCS components, which is unexpected. Specifying a combination of admission parameters that are likely to translate into a given clinical outcome is appealing from a clinician's perspective therefore our results have considerable translational value.

Indexed as

AlgorithmsBrain Injuries, TraumaticModels, NeurologicalAdultData AnalysisDatasets as TopicFemaleHumansLogistic ModelsMaleMiddle AgedModels, StatisticalPredictive Value of TestsPrognosisRisk

Identifiers

PMID29854144
PMCPMC5977564

What OpenQuestion holds

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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.