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Record W4410186780 · doi:10.2196/preprints.76995

Application of Commonly Used Physical Tests in Patients with Concussion to Patients with Various Types and Severities of Acquired Brain Injury: a Method Comparison Study (Preprint)

2025· preprint· en· W4410186780 on OpenAlexaboutno aff
Keely Barnes, Heidi Sveistrup, Mark Bayley, Michel P. Rathbone, Monica Taljaard, Mary Egan, Martin Bilodeau, Motahareh Karimijashni, Shawn Marshall

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsConcussionPreprintPhysical medicine and rehabilitationMedicinePhysical therapyComputer scienceInjury preventionPoison controlEmergency medicine

Abstract

fetched live from OpenAlex

BACKGROUND People who sustain a concussion and live in remote areas can experience challenges to 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 post-concussion virtually. OBJECTIVE The objectives of this method-comparison study were to determine i) inter/intra-rater reliability of a battery of concussion physical tests administered virtually in people with brain injury and ii) sensitivity and specificity of the virtual battery when compared to the in-person assessment. METHODS Sixty 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, 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 one-month intervals. RESULTS The sensitivity metrics ranged from moderate (60%) to excellent (100%) for saccades and cervical spine lateral flexion measures, respectively. Specificity ranged from 75% to 100% for left single leg stance eyes closed and left finger-to-nose testing, respectively. The inter-rater reliability ranged from poor for cervical spine extension (Cohen’s Kappa = 0.20) to excellent for VOMS change in symptoms (Cohen’s Kappa = 0.93). The intra-rater reliability ranged from poor for cervical spine extension (Cohen’s Kappa = 0.31) to excellent for the finger-to-nose testing on the right (Cohen’s Kappa = 0.90). 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 most promising properties when administered virtually. Caution should be maintained when administering certain concussion measures virtually. INTERNATIONAL REGISTERED REPORT RR2-10.2196/57663

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.011
metaresearch head score (Gemma)0.031
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 score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.378
Teacher spread0.346 · 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

Labeled directly by 2 models reading the full record.

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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