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Record W3132247007 · doi:10.1089/neur.2020.0035

Changes in White Matter of the Cervical Spinal Cord after a Single Season of Collegiate Football

2021· article· en· W3132247007 on OpenAlexaff
Brian Johnson, Alexa E. Walter, James R. Wilkes, Linda Papa, Semyon Slobounov

Bibliographic record

VenueNeurotrauma Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsWhite matterFractional anisotropySpinal cordDiffusion MRIConcussionSpinal cord injuryMedicinePoison controlMagnetic resonance imagingInjury preventionRadiology

Abstract

fetched live from OpenAlex

The involvement of the central nervous system (CNS), specifically the white matter tracts in the cervical spinal cord, was examined with diffusion tensor imaging (DTI) following exposure to repetitive head acceleration events (HAEs) after a single season of collegiate football. Fifteen National Collegiate Athletic Association (NCAA) Division 1 football players underwent DTI of the cervical spinal cord (vertebral level C1–4) at pre-season (before any contact practices began) and post-season (within 1 week of the last regular season game) intervals. Helmet accelerometer data were also collected in parallel throughout the season. From pre-season to post-season, a significant decrease ( p < 0.05) of axial diffusivity was seen within the right spino-olivary tract. In addition, a significant decrease ( p < 0.05) in global white matter fractional anisotropy (FA) along with increases ( p < 0.05) in global white matter mean diffusivity (MD) and radial diffusivity (RD) were found. These changes in FA from pre-season to post-season were significantly moderated by previous concussion history ( p < 0.05) and number of HAEs over 80 g ( p < 0.05). Despite the absence of sports-related concussion (SRC), we present measurable changes in the white matter integrity of the cervical spinal cord suggesting injury from repetitive HAEs, or SRC, may include the entirety of the CNS, not just the brain.

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)0.000
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 score0.026
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.069
GPT teacher head0.338
Teacher spread0.269 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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