Evidence map›Paper›PMID 39523284›Full record

ReviewAdvances in experimental medicine and biology2024

Computational Prognostic Modeling in Traumatic Brain Injury.

Matthew Pease, Dooman Arefan, Flora M Hammond, James F Castellano, David O Okonkwo, Shandong Wu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Advances in experimental medicine and biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Matthew PeaseDepartment of Neurosurgery, Indiana University, Indianapolis, IN, USA.
Dooman ArefanDepartment of Radiology, University of Pittsburgh, Pittsburgh, PA, USA.
Flora M HammondDepartment of Physical Medicine & Rehabilitation, Indiana University, Indianapolis, IN, USA.
James F CastellanoDepartment of Neurology, University of Pittsburgh, Pittsburgh, PA, USA.
David O OkonkwoDepartment of Neurosurgery, University of Pittsburgh, Pittsburgh, PA, USA.
Shandong WuDepartment of Radiology, University of Pittsburgh, Pittsburgh, PA, USA. wus3@upmc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traumatic brain injury is the leading cause of death and disability worldwide. Despite this large impact, no predictive models are in widespread use due to tedious data collection requirements, lack of provider trust, and poor performance. Furthermore, these models use simple, often binary, data elements that fail to capture the complex heterogeneity of traumatic brain injury. Recent advances in computational modeling efforts have demonstrated promising results for capturing imaging, clinical, electroencephalographic, and other biomarkers for powerful predictive models. In this review, we provide an overview of efforts in computational modeling in neurotrauma and provide insights into future directions.

Indexed as

Brain Injuries, TraumaticComputer SimulationBiomarkersElectroencephalographyHumansPrognosisBiomarkersArtificial intelligenceImagingMachine learningRecoveryTraumatic brain injury

Identifiers

PMID39523284

What OpenQuestion holds

Textmetadata
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.