Psychometric Properties of Vestibular and Ocular Measures Used for Concussion Assessments: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Concussions most commonly affect the vestibular and ocular systems. Clinical measures used in the assessment of vestibular and ocular deficits should contain strong psychometric properties so that clinicians can accurately detect abnormality to guide treatment interventions. OBJECTIVES: The aim of this scoping review was: (1) to identify the measures used to evaluate the vestibular and ocular domains postconcussion and (2) to document the psychometric properties of the measures. METHODS: Two databases (Medline (Ovid) and Embase) were searched from inception to May 2023. An updated search was completed in January 2024 using the same databases and search terms. Studies were screened and data were extracted independently by 2 reviewers. Measures were categorized into vestibular, ocular, or both (vestibular and ocular) domains, and relevant psychometric properties were documented. RESULTS: Fifty-two studies were included in this review. 28 studies explored the use of vestibular measures, 12 explored ocular measures, and 12 explored both vestibular and ocular measures or explored the use of vestibulo-ocular reflex measures. Most studies explored the properties associated with balance measures, particularly the balance error scoring system. Diagnostic accuracy (sensitivity and specificity metrics) of the associated measures was the most frequently documented characteristic in the literature. CONCLUSION: Identification of clinical measures used to evaluate vestibular and ocular deficits postconcussion is needed to understand the evidence supporting their use in practice. Documenting the psychometric properties will allow clinicians and researchers to understand the status of the current literature and support for the use of certain measures in practice in terms of their ability to appropriately detect deficits in people with concussion when deficits are truly present.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".