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Record W4353044697 · doi:10.4018/ijeach.319811

Evaluating the Effectiveness of Boxing Headguards in Mitigating Head Impact Accelerations That Cause Concussions by Using a Dynamic Head Model

2023· article· en· W4353044697 on OpenAlexafffund
Tyson R. Rybak, Paolo Sanzo, Meilan Liu, Carlos Zerpa

Bibliographic record

VenueInternational Journal of Extreme Automation and Connectivity in Healthcare · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsLakehead University
FundersLakehead University
KeywordsHead (geology)ConcussionAthletesComputer sciencePoison controlEngineeringPhysical medicine and rehabilitationAeronauticsForensic engineeringInjury preventionPhysical therapyMedicineGeologyEmergency medicine

Abstract

fetched live from OpenAlex

Boxing headguards offer a form of head protection to minimize the risk of head injuries for athletes. Existing literature, however, lacks information regarding the protective capabilities of boxing headguards. This study examined the protective capacity of three boxing headguards in minimizing impact accelerations to the head that cause concussions using a dynamic head model. The researchers implemented thermoplastic polyurethane (TPU) inserts in one of the headguards and conducted static tests to examine the material properties of the headguards. The researchers also conducted dynamic testing using a surrogate headform to compare the three boxing headguards in minimizing the risk of concussion for measures of linear and rotational accelerations across different head impact locations. The results of this study revealed that TPU significantly mitigated the magnitude of linear and rotational accelerations when compared to the other headguards. This study offers an avenue to improve athlete safety.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
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 integrity0.0000.001
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 head0.349
GPT teacher head0.537
Teacher spread0.188 · 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.

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
Published2023
Admission routes2
Has abstractyes

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