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Record W4415648706 · doi:10.2196/76995

Application of Commonly Used Physical Tests in a Virtual Environment in Patients With Concussion to Patients With Various Types and Severities of Acquired Brain Injury: Prospective Cohort Method Comparison Study

2025· article· en· W4415648706 on OpenAlexaffabout
Keely Barnes, Heidi Sveistrup, Mark Bayley, Michel P. Rathbone, Monica Taljaard, Mary Egan, Martin Bilodeau, Motahareh Karimijashni, Shawn Marshall

Bibliographic record

VenueJournal of Medical Internet Research · 2025
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsMcMaster UniversityHamilton Health SciencesUniversity of TorontoToronto Rehabilitation InstituteCarleton UniversityBruyèreUniversity of OttawaOttawa Hospital
Fundersnot available
KeywordsConcussionProspective cohort studyCohortTest (biology)Virtual machineCohort studyPoison controlInjury preventionVirtual reality

Abstract

fetched live from OpenAlex

Background: People who sustain a concussion and live in remote areas can experience challenges in accessing specialized assessments. In these cases, virtual approaches to assessment are of value. There is limited information on important psychometric properties of physical assessment measures used to evaluate people postconcussion virtually. Objective: The aims of this method-comparison psychometric study were to determine (1) inter- and intrarater reliability of a battery of concussion physical tests administered virtually in people with brain injury and (2) sensitivity and specificity of the virtual battery when compared to the in-person assessment. Methods: A total of 60 people living with acquired brain injuries attended an in-person and virtual assessment at the Ottawa Hospital Rehabilitation Centre. The order of the assessments, in-person and virtual, was randomized. The following physical measures were administered in-person and virtually: finger-to-nose test, vestibular ocular motor screening (VOMS), static balance testing (double leg, single leg, and tandem), saccades, cervical spine range of motion, and evaluation of effort. The virtual assessment was recorded, and a second clinician viewed and independently documented findings from the recordings twice at 1-month intervals. Results: The mean age of the participants was 45.65 (SD 16.50) years. The sensitivity metrics ranged from moderate (60%, 95% CI 30-86) to excellent (100%, 95% CI 71-100) for saccades and cervical spine right lateral flexion, respectively. Specificity ranged from 75%, 95% CI 35-95 to 100%, 95% CI 91-100 for left single leg stance eyes closed and left finger-to-nose testing, respectively. The interrater reliability ranged from poor for cervical spine extension (Cohen κ=0.20, 95% CI -0.07 to 0.47) to excellent for VOMS change in symptoms (Cohen κ=0.93, 95% CI 0.83-1). The intrarater reliability ranged from poor for cervical spine extension (Cohen κ=0.31, 95% CI 0.04-0.58) to excellent for the finger-to-nose testing on the right (Cohen κ=0.90, 95% CI 0.71-1). The wide CIs highlight variability in precision and suggest that further research with larger samples is needed before clinical use can be fully standardized. Conclusions: This study provides information on the psychometric properties associated with virtual administration of concussion measures. The VOMS change in symptoms measure appears to have the most promising properties when administered virtually when in-person visits are not possible. This is particularly relevant for patients in rural areas, for those facing access barriers, and in contexts where timely follow-up is challenging. However, caution should be maintained when administering certain concussion measures virtually. The wide CIs for some measures caution against over-reliance on single test findings, and clinicians should consider both the strengths and limitations of virtual delivery. Clinicians are encouraged to make informed decisions about which measures can be effectively used remotely, and which may still require in-person administration to maintain accuracy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.416
Teacher spread0.385 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2025
Admission routes2
Has abstractyes

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