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Record W2963042632 · doi:10.1177/1460408619861026

A preliminary analysis of biomechanics and saccadic responses for concussion

2019· article· en· W2963042632 on OpenAlexafffund
Andrew Post, Eyesha Hashim, Windsor Kwan‐Chun Ting, T. Blaine Hoshizaki, Michael D. Gilchrist, Michael D. Cusimano

Bibliographic record

VenueTrauma · 2019
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of TorontoUniversity of OttawaSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsSaccadic maskingMedicineConcussionBiomechanicsDiffusion MRIPhysical medicine and rehabilitationEye movementPoison controlAnatomyOphthalmologyInjury preventionMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Background Concussion is an injury that occurs in non-sporting and sporting environments. To determine improved clinical methods for identifying this injury, it is important to develop and understand how the impact event results in quantifiable differences in brain functioning—functioning that has been quantified in the past using saccadic measures. The purpose of this research was to examine the relationships between oculomotor deficits, specifically antisaccade responses, and the biomechanics of impact for a concussion. Methods Participants underwent a diffusion tensor imaging protocol as well as saccadic testing to determine differences in brain functioning in comparison to controls. The injury event was then reconstructed in laboratory using physical and finite element models to determine the biomechanics of the impact and brain tissue strain. Relationships between the biomechanical variables and antisaccade responses were then examined. Results The diffusion tensor imaging analyses found that there was a decrease in radial diffusivity and axial diffusivity found in the cerebral peduncle (p < 0.05) and cingulum hippocampus (p < 0.05), respectively. There was an increase in the axial diffusivity for the corona radiata (p < 0.05). The saccadic testing found an increase in mean latency for the concussed group (p < 0.05). The results indicated no significant relationship between mean latency, duration, amplitude and peak velocity antisaccade measures and the biomechanical variables. This may have been influenced not only by a lack of sensitivity in biomechanical variable to antisaccade responses, but also to these responses being affected by factors other than injury such as attentiveness and wakefulness. Conclusion While the sample of this research was small, this research suggests that to improve the understanding of the relationship between impact biomechanics and concussion, methods that can quantify the damage to brain structures through imaging, such as diffusion tensor imaging, may be more appropriate.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.071
GPT teacher head0.373
Teacher spread0.302 · 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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Citations3
Published2019
Admission routes2
Has abstractyes

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