Evidence map›Paper›PMID 40678189›Full record

ArticleInternational journal of clinical and health psychology : IJCHP

Optimizing pediatric "Mild" traumatic brain injury assessments: A multi-domain random forest analysis of diagnosis and outcomes.

Upasana Nathaniel, Erik B Erhardt, Divyasree Sasi Kumar, Jingshu Wu, Samuel D Miller, Pawani Chauhan, Rahsan Keskin, Tracey V Wick, Keith Owen Yeates, Timothy B Meier and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of clinical and health psychology : IJCHP. 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

15 authors.

Upasana NathanielThe Mind Research Network/Lovelace Biomedical Research Institute, Albuquerque, NM 87106, USA.
Erik B ErhardtDepartment of Mathematics and Statistics, University of New Mexico, Albuquerque, NM 87131, USA.
Divyasree Sasi KumarThe Mind Research Network/Lovelace Biomedical Research Institute, Albuquerque, NM 87106, USA.
Jingshu WuThe Mind Research Network/Lovelace Biomedical Research Institute, Albuquerque, NM 87106, USA.
Samuel D MillerThe Mind Research Network/Lovelace Biomedical Research Institute, Albuquerque, NM 87106, USA.
Pawani ChauhanThe Mind Research Network/Lovelace Biomedical Research Institute, Albuquerque, NM 87106, USA.
Rahsan KeskinDepartment of Physical Medicine & Rehabilitation, University of New Mexico, Albuquerque, NM 87131, , USA.
Tracey V WickThe Mind Research Network/Lovelace Biomedical Research Institute, Albuquerque, NM 87106, USA.
Keith Owen YeatesDepartment of Psychology, University of Calgary, Calgary, Alberta, Canada.
Timothy B MeierNeurosurgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Harm J van der HornThe Mind Research Network/Lovelace Biomedical Research Institute, Albuquerque, NM 87106, USA.
John P PhillipsThe Mind Research Network/Lovelace Biomedical Research Institute, Albuquerque, NM 87106, USA.
Richard A CampbellDepartment of Psychiatry & Behavioral Sciences, University of New Mexico, Albuquerque, NM 87131.
Robert E SapienDepartment of Emergency Medicine, University of New Mexico, Albuquerque, NM 87131, USA.
Andrew R MayerThe Mind Research Network/Lovelace Biomedical Research Institute, Albuquerque, NM 87106, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Despite advances in imaging and fluid-based biomarkers, the care for pediatric "mild" traumatic brain injury (pmTBI) remains primarily dependent on clinical evaluation. However, the optimal clinical assessments for diagnosing pmTBI and predicting outcomes remain debated, including which individual test or combinations of assessments are most effective, and how this evolves as a function of time post-injury. Method: Random Forest models were used to identify the most effective assessments for diagnostic (pmTBI vs. healthy controls) and outcome (pmTBI with favorable vs. poor outcomes, based on persisting symptoms) classification accuracy across a comprehensive battery including domains of self-reported clinical-ratings, paper-and-pencil cognitive tests, computerized cognitive tests, symptom provocation during neurosensory tests, and performance-based neurosensory measures. Assessments were conducted within 11-days, at 4-months and 1-year post-injury to examine acute and long-term recovery trajectories. A total of 323 pmTBI (180 males; age 14.5 ± 2.8 years) and 244 HC (134 males, 14.0 ± 2.9 years) were included (∼75 % 1-year retention) in final analyses. Results: Self-reported clinical-ratings outperformed performance-based metrics across all visits in both models, with somatic complaints demonstrating the highest predictive validity. Cognitive tests of memory aided diagnostic classification, while emotional disturbances were predictive of outcome classification up-to 4-months. Retrospective ratings, reflecting trait-like characteristics, were more predictive for identifying individuals at risk of poor outcomes. Computerized cognitive and neurosensory tests had limited predictive value beyond 1-week post-injury. Conclusions: Clinicians should adopt a tailored approach for clinical assessments across different post-injury intervals to enhance clinical care, shorten assessment batteries, and better understand recovery in children with "mild" TBI.

Indexed as

Clinical assessmentsDiagnostic classificationMachine learningMild traumatic brain injuryOutcome classificationPediatric

Identifiers

PMID40678189
PMCPMC12269848

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.