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Smooth Pursuit Eye Movement Impairments Following SRC Compared To Healthy Controls.

2021· article· en· W3181754213 on OpenAlexaboutno aff
Madison R. Taylor, Marie Kelly, Lauren Netzel, Dustin Hopfe, Phil Pavilionis, Brian Székely, Nora Constantino, Nicholas G. Murray

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2021
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsSmooth pursuitEye movementAudiologyPsychologyPhysical medicine and rehabilitationVisual acuityChinMedicineTask (project management)OphthalmologyNeuroscienceAnatomy

Abstract

fetched live from OpenAlex

The brainstem, which includes the midbrain, have been recently implicated as a site of potential axonal damage following a sport-related concussion (SRC). These areas of the brain are involved in the appropriate execution of eye movements. If impaired, oculomotor control could be influenced which may lead to a decline in motion perception. Unfortunately, information is lacking regarding the influence of SRC on specific eye movements during a dynamic visual acuity (DVA) task. PURPOSE: The purpose of this study was to compare smooth pursuit eye movements (SPEMs) and saccades among athletes with SRC and healthy controls during a DVA task. METHODS: 13 NCAA Division 1 SRC (avg. age = 20 years) and 13 healthy controls (avg. age = 21 years) performed an SVA and an adaptive DVA task. Athletes with SRC were diagnosed by the head team physician and assessed within 24-48 hours. For the SVA task, participants performed a series of acuity Es from a distance of 150 cm. For the DVA task, participants tracked a Landolt C during 120 randomized trials (60 trials of smooth pursuit at 30°/s [SPEM], and 60 trials of saccades at 90°/s). Participants were fitted with a head-mounted binocular video oculography eye tracker (Eyelink SR Research, 500 Hz, Ottawa, CN), with their chin stabilized 154 cm away from the 26° visual field monitor (165 Hz, 2560 x 1440 pixel resolution, 300 cd/m2 luminance). Using a 2-up-1-down staircase method, the participants were asked to track the Landolt-C that moved horizontally left to right across the center of the screen, in addition to answering what direction the Landolt C was facing (up, down, left, or right) using the arrow keys. SPEM velocity gain and saccadic peak velocity were calculated and compared among groups. RESULTS: SPEM gain for SRC (1.07 + 0.05) was significantly higher than the control group (1.04 + 0.03, p = 0.038, Cohen’s d = 0.86) while saccadic peak velocity was not different between the groups (SRC = 405 + 38.2 m/s, control = 417 + 47.2 m/s; p = 0.507). CONCLUSIONS: While both groups demonstrated higher than average SPEM gain, which varies from 0.95 (lagging behind target) to 1.0 (directly on target); SRC may cause an increase of SPEM gain. These data indicate that athletes with SRC may lead a visual target with their eyes during an adaptive DVA task.

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)0.001
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 score0.007
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.043
GPT teacher head0.370
Teacher spread0.327 · 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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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