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Record W4393180262 · doi:10.1093/milmed/usae065

Comparison of Vestibular/Ocular Motor Screening (VOMS) and Computerized Eye-tracking to Identify Exposure to Repetitive Head Impacts

2024· article· en· W4393180262 on OpenAlexaboutno aff
Anthony P. Kontos, Aaron J. Zynda, Amir Minerbi

Bibliographic record

VenueMilitary Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsVestibular systemConcussionMedicineAffect (linguistics)Poison controlOccupational safety and healthInjury preventionMilitary medicinePhysical medicine and rehabilitationAudiologyPhysical therapyPsychologyEmergency medicinePathology

Abstract

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INTRODUCTION: Military service members (SMs) are exposed to repetitive head impacts (RHIs) in combat and training that are purported to adversely affect brain health, including cognition, behavior, and function. Researchers have reported that RHI from blast-related exposure may affect both vestibular and ocular function, which in turn may be related to symptomology. As such, an examination of the effects of RHI on exposed military SMs should incorporate these domains. To date, researchers have not compared groups of exposed special operations forces (SOF) operators on combined clinical vestibular/ocular and eye-tracker-based outcomes. Therefore, the primary purpose of this study was to compare participant-reported symptoms and performance on the Vestibular/Ocular Motor Screening (VOMS) tool with performance on the computerized RightEye tracking system between SOF operators exposed to blast-related RHI and healthy controls without blast-related exposure. In addition, the study aimed to compare subgroups of snipers and breachers exposed to RHI to controls on the preceding metrics, as well as identify a subset of individual (demographic) factors, participant-reported symptoms, and performance metrics on VOMS and RightEye that best identify SOF operators exposed to RHI from unexposed controls. MATERIALS AND METHODS: The study involved a cross-sectional design including 25 Canadian SOF SMs comprised of breachers (n = 9), snipers (n = 9), and healthy, unexposed controls (n = 7). The former 2 groups were combined into an RHI group (n = 18) and compared to controls (n = 7). Participants provided demographics and completed a self-reported concussion-related symptom report via the Military Acute Concussion Evaluation 2, the VOMS, and RightEye computerized eye-tracking assessments. Independent samples t-tests and ANOVAs were used to compare the groups on the outcomes, with receiver operating characteristic curve and area under the curve (AUC) analyses to identify predictors of blast exposure. This study was approved by the Defence Research Development Canada Human Research Ethics Committee and the Canadian Forces Surgeon General/Special Forces Command. RESULTS: The results from t-tests supported group differences for age (P = .012), participant-reported symptoms (P = .006), and all VOMS items (P range = <.001-.02), with the RHI group being higher than healthy controls on all variables. ANOVA results supported group differences among snipers, breachers, and controls for age (P = .01), RightEye saccades (P = .04), participant-reported total symptom severity (P = .03), and VOMS total scores (P = .003). The results of the receiver operating characteristic curve analyses supported age (AUC = 0.81), Military Acute Concussion Evaluation 2 participant-reported total symptom severity (AUC = 0.87), and VOMS total scores (AUC = 0.92) as significant predictors of prior blast exposure. CONCLUSIONS: Participant-reported concussion symptoms, VOMS scores, and age were useful in identifying SOF operators exposed to RHI from controls. RightEye metrics were not useful in differentiating RHI groups from controls. Differences between snipers and breachers warrant further research. Overall, the findings suggest that VOMS may be a useful tool for screening for the effects of exposure to RHI in SOF operators. Future investigations should be conducted on a larger sample of military SMs, consider additional factors (e.g., RHI exposure levels, medical history, and sex), and include additional assessment domains (e.g., balance, cognitive, and psychological).

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.104
GPT teacher head0.468
Teacher spread0.364 · 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 teacher head, not a consensus.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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