Evidence map›Paper›PMID 28443058›Full record

ArticleFrontiers in neurology2017

Predicting Outcome 12 Months after Mild Traumatic Brain Injury in Patients Admitted to a Neurosurgery Service.

Torgeir Hellstrøm, Tobias Kaufmann, Nada Andelic, Helene L Soberg, Solrun Sigurdardottir, Eirik Helseth, Ole A Andreassen, Lars T Westlye

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Frontiers in neurology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05407948 (The Interaction Between Cognitive Reserve and Neuropsychological Variables, Variables Related to Psychological Well-being and Health Care Usage in Relation to Long-term Outcome After Stroke or Traumatic Brain Injury), which is not on this map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.8field-weighted citation impact, top 10% 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.

NCT05407948 completednot on this mapstarted 2018, after this paper: background citation

The Interaction Between Cognitive Reserve and Neuropsychological Variables, Variables Related to Psychological Well-being and Health Care Usage in Relation to Long-term Outcome After Stroke or Traumatic Brain Injury

TypeobservationalSponsorNatascha EkdahlRan2018 to 2022Enrolled87ConditionsTraumatic Brain Injury, Stroke
3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 38 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Machine Learning Approaches to Prognostication in Traumatic Brain Injury.Current neurology and neuroscience reports · 2025
    Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Review
  15. Review
  16. 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

8 authors at 3 institutions in 1 country.

Torgeir HellstrømDepartment of Physical Medicine and Rehabilitation, Oslo University Hospital, Oslo, Norway.
Tobias KaufmannKG Jebsen Centre for Psychosis Research/Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
Nada AndelicDepartment of Physical Medicine and Rehabilitation, Oslo University Hospital, Oslo, Norway.
Helene L SobergDepartment of Physical Medicine and Rehabilitation, Oslo University Hospital, Oslo, Norway.
Solrun SigurdardottirSunnaas Rehabilitation Hospital, Nesoddtangen, Norway.
Eirik HelsethFaculty of Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
Ole A AndreassenKG Jebsen Centre for Psychosis Research/Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
Lars T WestlyeKG Jebsen Centre for Psychosis Research/Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
Oslo University Hospital · NOSunnaas sykehus · NOUniversity of Oslo · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveAccurate outcome prediction models for patients with mild traumatic brain injury (MTBI) are key for prognostic assessment and clinical decision-making. Using multivariate machine learning, we tested the unique and added predictive value of (1) magnetic resonance imaging (MRI)-based brain morphometric and volumetric characterization at 4-week postinjury and (2) demographic, preinjury, injury-related, and postinjury variables on 12-month outcomes, including global functioning level, postconcussion symptoms, and mental health in patients with MTBI.

methodsA prospective, cohort study of patients (

resultsWe found a strong correlation between observed and predicted outcomes for the clinical model (

conclusionWe found no added predictive value of MRI-based measures of brain cortical morphometry and subcortical volumes over and above demographic and clinical features.

Indexed as

cortical thicknessoutcomepredictiontraumatic brain injuryvolumetric analysis

Identifiers

PMID28443058
PMCPMC5385465
OpenAlexW2606183213

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.