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Dynamic Visual Acuity Test To Denote Visuomotor Changes In Adolescents Participating In High Impact Sports

2022· article· en· W4294844977 on OpenAlexaboutno aff
Vincentia Owusu-Amankonah, Madison R. Taylor, Philip Pavilionis, Dustin Hopfe, Lauren Netzel, Joseph McCarley, Sonya L. Kirby, Nora Constantino, Nicholas G. Murray

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2022
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsConcussionFootballPsychologySmooth pursuitEye movementVisual acuitySaccadic maskingPhysical medicine and rehabilitationFixation (population genetics)AthletesPoison controlInjury preventionMedicinePhysical therapyOphthalmologyGeography

Abstract

fetched live from OpenAlex

Repetitive head impacts (RHI), independent of concussion, can have a negative influence on the visual system but data are sparse in adolescent contact athletes. PURPOSE: Evaluate the effect of repetitive head impacts influence on adolescent football players oculomotor and vision throughout the mid-point of a single contact season using a dynamic visual acuity (DVA) task. METHODS:4 NIAA Varsity and JV football high school players (all males) with ≤20/20 static visual acuity with or without corrective lenses participated in this study. Student-athletes were evaluated prior (PRE) to the start of the season and in the middle (MID) of the season. The football players all wore instrumented mouthguards to track head impacts during each practice and game. Every participant completed 120 randomized trials of smooth pursuit (SPEM) (30°/s) and saccadic (90°/s) eye movements at (PRE) and (MID) of the season. All participants eye movements were tracked by (500 Hz; Eyelink SR research, Ottawa, CN) while their heads were stable in a chin rest 150 cm away from a 26° monitor. Participants were tasked to determine the orientation of Landolt-C’s openings (left, right, up, down) that moved across the monitor (horizontally left to fight) using a keypad. Depending on the correct/incorrect responses during the smooth pursuit and saccadic trials, the size of the C’s would either increase or decrease in size (2-up, 1-down staircase). Between each trial, a retinal flush image was presented for 2 ms before the appearance of a fixation cross. Smoot pursuit eye movement (SPEM) gain, and vision alongside saccadic peak velocity were evaluated at each time point. RESULTS: No significant difference was noted at PRE (1.09 ± 0.04) to MID (1.07 ± 0.08; p = 0.33, Cohen’s d = 0.32) for SPEM gain and for saccadic peak velocity (PRE = 386.70 ± 36.94 m/s, MID = 397.20 ± 2.12 m/s; p = 0.935, Cohen’s d = 0.33). Similarly, no difference was noted for SPEM Vision at PRE (20/32) and MID (20/32; p = 0.71) and for saccades (PRE = 20/40, MID = 20/40; p = 0.935). Participants had ≤20/20 static visual acuity at both time points. CONCLUSIONS: Oculomotor control and vision do not decline in the presence of RHI from the beginning to the middle of a contact football season in adolescents. Additional data are needed given the strength of the effect size.

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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.389
Teacher spread0.352 · 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
Published2022
Admission routes1
Has abstractyes

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