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Comparison of Clinical and Technological Vestibular and Visual Assessments in Moderate/Severe Traumatic Brain Injury Patients

2017· other· en· W6946047376 on OpenAlexaboutno aff

Bibliographic record

VenueBiblioBoard Library Catalog (Open Research Library) · 2017
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTraumatic brain injuryQuality of life (healthcare)ClearanceInjury preventionPoison controlBalance (ability)Occupational safety and healthHuman factors and ergonomics

Abstract

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Introduction: The World Health Organization predicts that by 2020 traumatic brain injuries (TBI) will be one of the most common causes of death and persistent injuries worldwide. Visual and vestibular deficits are particularly prevalent following TBI. Due to limitations of current assessment techniques, these deficits often go unnoticed by clinicians. The purpose of this study was to identify alterations in oculomotor, vestibular, and dynamic visual attention that occur following TBI and to investigate whether novel technological assessments would improve cliniciansu2019 abilities to detect these TBI-induced deficits.Methods: A convenience sample of ten participants who had suffered a severe traumatic brain injury between the ages of 18-50 years were invited to participate. This case series, feasibility and validation study is part of a transnational collaborative research program evaluating vestibulo-ocular deficits across all ages and the TBI severity spectrum (with sites in Calgary, Montreal, Paris, and Tel-Aviv). The Quality of Life after Brain Injury (QOLIBRI), Post-Concussion Symptom Inventory (PCSI), and Dizziness Handicap Inventory (DHI) questionnaires were administered. All participants underwent a neurological and cervical exam as well as clinical and technological oculomotor, vestibular, and balance assessments. The Neurotracker was used to assess dynamic visual attention. All assessments were performed as soon as possible once medically cleared for rehabilitation. Descriptive statistics were run for demographic information and primary outcome measures.Results: Ten inpatients (9 males; 1 female) with a median age of 37.67 years (IQR 31.71-46.90 years) with severe TBI in Calgary, Alberta, Canada have completed the assessments. The participants had a median of 14.00 years of education (IQR 13.00-15.25 years). Median time since injury was 39.12 days (IQR 25.85- 56.03 days) with a median Glasgow Coma Scale score thirty minutes post-injury of 4 (IQR 3-5.5). 7/10 reported increased balance difficulties and 5/10 participants reported increased visual problems post-injury on the PCSI. 8/10 participants reported dizziness on the DHI with a median score of 22 (IQR 3-28). Cervical spine fracture, poor static visual acuity, facial fractures, skull sensitivity and orthopedic injuries limited which tests could be performed. Primary outcome measures of clinical versus technological assessments aligned in 1/6 for vestibular (clinical dynamic visual acuity test, InVision dynamic visual acuity test), 6/9 for oculomotor (clinical saccade test, Otometrics saccade test), and 7/9 for balance (Balance Error Scoring System, National Institutes of Health Toolbox Standing Balance Test). Median Neurotracker threshold score was 0.58 m/s (IQR 0.07-0.78).Conclusion: Preliminary analyses suggest that technological evaluation of vestibulo-ocular deficits following TBI extends current clinical assessments. Limitations to testing included cervical spine fracture, poor static visual acuity, facial fractures, skull sensitivity and orthopedic injuries. Further research is required to investigate the technological measures for assessment of vestibulo-ocular impairments in the adult severe TBI population.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.590
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.002
Science and technology studies0.0000.003
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.191
GPT teacher head0.468
Teacher spread0.277 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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