Evidence map›Paper›PMID 41192220›Full record

ArticleEBioMedicine2025

Beyond mild, moderate, and severe traumatic brain injury: modelling severity from clinical, neuroimaging, and blood-based indicators.

Lindsay D Nelson, Brooke E Magnus, John K Yue, Steve Balsis, Christopher J Patrick, Nancy Temkin, Esther L Yuh, Ramon Diaz-Arrastia, Ehri Ryu, Andrew I R Maas and 5 more

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Article in EBioMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Lindsay D NelsonDepartments of Neurosurgery & Neurology, Medical College of Wisconsin, Milwaukee, WI, USA. Electronic address: linelson@mcw.edu.
Brooke E MagnusDepartment of Psychology and Neuroscience, Boston College, Boston, MA, USA.
John K YueDepartment of Neurosurgery, University of California, San Francisco, CA, USA.
Steve BalsisDepartment of Psychology, University of Massachusetts Lowell, Lowell, MA, USA.
Christopher J PatrickDepartment of Psychology, Florida State University, Tallahassee, FL, USA.
Nancy TemkinDepartments of Neurological Surgery and Biostatistics, University of Washington, Seattle, WA, USA.
Esther L YuhDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
Ramon Diaz-ArrastiaDepartment of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
Ehri RyuDepartment of Psychology and Neuroscience, Boston College, Boston, MA, USA.
Andrew I R MaasDepartment of Neurosurgery, University of Antwerp; and Antwerp University Hospital, Edegem, Belgium.
David K MenonDepartment of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom.
Lindsay WilsonDivision of Psychology, School of Natural Sciences, University of Stirling, Stirling, United Kingdom.
Geoffrey T ManleyDepartment of Neurosurgery, University of California, San Francisco, CA, USA.
TRACK-TBI Investigators
CENTER-TBI Participants and Investigators

Funding

Improving Patient Classification and Outcome Measurement in Traumatic Brain Injury (TBI)R01NS110856 · NINDS · MEDICAL COLLEGE OF WISCONSIN · PI NELSON, LINDSAY · 2019 to 2023
$3.0M
NINDS NIH HHS R01 NS110856
6 · The paper itself

Abstract

backgroundThe conventional clinical approach to characterising traumatic brain injuries (TBIs) as mild, moderate, or severe using the Glasgow Coma Scale (GCS) total score has well-known limitations, prompting calls for more sophisticated strategies.

methodsWe used item response theory (IRT) to develop a new method for quantifying TBI severity using 24 clinical, head computed tomography, and blood-based biomarker variables familiar to clinicians and researchers. IRT uses individuals' response patterns across indicators to estimate relationships between the indicators and a latent continuum of TBI severity. Model parameters were used to assign severity scores in two large cohorts, and associations with traditional GCS categories and 6-month functional outcomes (Glasgow Outcome Scale-Extended [GOSE]) were tested with correlational and logistic regression analyses.

findingsIn the prospective Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) cohort (N = 2545), modelling showed the 24 indicators index a common latent continuum of TBI severity. IRT enabled us to identify the relative contribution of these features to estimate an individual's TBI severity. Finally, within both the TRACK-TBI derivation sample and an external validation sample (Collaborative European NeuroTrauma Effectiveness Research in TBI [CENTER-TBI]), TBI severity scores generated using this novel IRT-based method incrementally predicted functional (GOSE) outcome better than classic clinical (mild, moderate, severe) or International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) classification methods.

interpretationOur findings directly inform ongoing international efforts to refine and deploy new pragmatic, empirically-supported strategies for characterising TBI, while illustrating a strategy that may be useful to improve staging systems for other diseases.

fundingThis secondary analysis project was funded by the U.S. National Institute of Neurological Disorders and Stroke (Grant No. R01 NS110856).

Indexed as

Brain Injuries, TraumaticNeuroimagingAdultAgedBiomarkersFemaleGlasgow Coma ScaleGlasgow Outcome ScaleHumansMaleMiddle AgedSeverity of Illness IndexTomography, X-Ray ComputedYoung AdultBiomarkersBlood-based biomarkersClassificationItem response theoryNeuroimagingSeverityTraumatic brain injury

Identifiers

PMID41192220
PMCPMC12637077

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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.