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Record W4404523371 · doi:10.1007/s40279-024-02138-6

Associations Between Instrumented Mouthguard-Measured Head Acceleration Events and Post-Match Biomarkers of Astroglial and Axonal Injury in Male Amateur Australian Football Players

2024· article· en· W4404523371 on OpenAlexaff
Lauren Evans, William T. O’Brien, Gershon Spitz, Steven Mutimer, Becca Xie, Lauren P. Giesler, Brendan P. Major, James W. Hickey, Spencer Roberts, Biswadev Mitra, Terence J. O’Brien, Sandy R. Shultz, Stuart J. McDonald

Bibliographic record

VenueSports Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsVancouver Island University
FundersNational Health and Medical Research Council
KeywordsConcussionMedicineGlial fibrillary acidic proteinFootballLinear accelerationMouthguardPoison controlInternal medicinePhysical therapyInjury preventionAccelerationPhysicsImmunohistochemistryGeographyEmergency medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Advances in instrumented mouthguards (iMGs) allow for accurate quantification of single high-acceleration head impacts and cumulative head acceleration exposure in collision sports. However, relationships between these measures and risk of brain cell injury remain unclear. AIM: The purpose of this study was to quantify measures of non-concussive head impact exposure and assess their association with blood glial fibrillary acidic protein (GFAP), neurofilament light (NfL) and phosphorylated-tau-181 (p-tau-181) levels in male Australian football players. METHODS: A total of 31 athletes underwent in-season (24 h post-match) and post-season (> 5 weeks) blood collections and/or wore HITIQ Nexus A9 iMGs measuring peak linear (PLA) and rotational (PRA) acceleration. Match footage was used to verify and code impacts. Blood GFAP, NfL, and p-tau-181 were quantified using Simoa and natural log transformed for analysis. Associations between post-match biomarkers and within match maximum single impact and cumulative PLA/PRA were assessed with linear mixed models. RESULTS: In-season versus post-season elevations were found for GFAP (mean difference 0.14, 95% CI 0.01-0.26, p = 0.033), NfL (mean difference = 0.21, 95% CI 0.09-0.32, p = 0.001) and p-tau-181 (mean difference = 0.49, 95% CI 0.33-0.65, p < 0.001). Post-match GFAP was associated with maximum single impact PLA (B = 0.003, 95% CI 0.0002-0.005, p = 0.036), cumulative PLA (B = 0.001, 95% CI 0.0002-0.002, p = 0.017), cumulative PRA (B = 0.01, 95% CI 0.002-0.02, p = 0.014), and impact number (B = 0.03, 95% CI 0.003-0.05, p = 0.029) within a single match. Change in NfL levels between two-matches correlated with cumulative PLA (r = 0.80, 95% CI 0.38-0.95, p = 0.005), PRA (r = 0.71, 95% CI 0.19-0.92, p = 0.019) and impact number (r = 0.63, 95% CI 0.05-0.89, p = 0.038). CONCLUSION: Maximum and cumulative head accelerations in Australian football, measured by iMGs, were associated with elevated blood biomarkers of brain injury, highlighting the potential of both technologies for head impact management in collision sports.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.059
GPT teacher head0.356
Teacher spread0.296 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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