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Plasma Biomarkers of Traumatic Brain Injury in Adolescents With Sport-Related Concussion

2024· article· en· W4402269304 on OpenAlexafffundabout
Jason Tabor, Linden C. Penner, Jean‐Michel Galarneau, Nik Josafatow, Jennifer Cooper, Mohammad Ghodsi, Johnny W. Huang, Douglas D. Fraser, Jonathan D. Smirl, Michael J. Esser, Keith Owen Yeates, Cheryl L. Wellington, Chantel T. Debert, Carolyn A. Emery

Bibliographic record

VenueJAMA Network Open · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of British ColumbiaHotchkiss Brain InstituteWestern UniversityAlberta Children's HospitalUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsConcussionMedicineBiomarkerTraumatic brain injuryInternal medicineCohortPhysical therapyProspective cohort studyPoison controlInjury preventionPsychiatryEmergency medicine

Abstract

fetched live from OpenAlex

Importance: Blood-based biomarkers may clarify underlying neuropathology and potentially assist in clinical management of adolescents with sport-related concussion (SRC). Objective: To investigate the association between SRC and plasma biomarkers in adolescents. Design, Setting, and Participants: Prospective cohort study in Canadian sport and clinic settings (Surveillance in High Schools and Community Sport to Reduce Concussions and Their Consequences study; September 2019 to November 2022). Participants were a convenience sample of 849 adolescent (ages 10-18 years) sport participants with blood samples. Data were analyzed from February to September 2023. Exposures: Blood collection and clinical testing preseason (uninjured) and post-SRC follow-ups (ie, ≤72 hours, 1 week, and biweekly until medical clearance to return to play [RTP]). Main Outcomes and Measures: Plasma glial fibrillary acidic protein (GFAP), ubiquitin c-terminal hydrolase-L1 (UCH-L1), neurofilament light (NfL), and total tau (t-tau) were assayed. Group-level comparisons of biomarker levels were conducted between uninjured and post-SRC intervals (postinjury day [PID] 0-3, 4-10, 11-28, and >28) considering age and sex as modifiers. Secondary analyses explored associations between biomarker concentrations and clinical outcomes (Sport Concussion Assessment Tool, Fifth Edition [SCAT5] symptom scores and time to RTP). Results: This study included 1023 plasma specimens from 695 uninjured participants (467 male participants [67.2%]; median [IQR] age, 15.90 [15.13-16.84] years) and 154 participants with concussion (78 male participants [51.0%]; median [IQR] age, 16.12 [15.31-17.11] years). Acute (PID 0-3) differences relative to uninjured levels were found for GFAP (female participants: 17.8% increase; β = 0.164; 95% CI, 0.064 to 0.263; P = .001; male participants: 17.1% increase; β = 0.157; 95% CI, 0.086 to 0.229; P < .001), UCH-L1 (female participants: 43.4% increase; β = 0.361; 95% CI, 0.125 to 0.596; P = .003), NfL (male participants: 19.0% increase; β = 0.174; 95% CI, 0.087 to 0.261; P < .001), and t-tau (female participants: -22.9%; β = -0.260; 95% CI, -0.391 to -0.130; P < .001; male participants: -18.4%; β = -0.203; 95% CI, -0.300 to -0.106; P < .001). Differences were observed for all biomarkers at PID 4 to 10, 11 to 28, and greater than 28 compared with uninjured groups. GFAP, NfL, and t-tau were associated with SCAT5 symptom scores across several PID intervals. Higher GFAP after 28 days post-SRC was associated with earlier clearance to RTP (hazard ratio, 4.78; 95% CI, 1.59 to 14.31; P = .01). Male participants exhibited lower GFAP (-9.7%), but higher UCH-L1 (21.3%) compared with female participants. Age was associated with lower GFAP (-5.4% per year) and t-tau (-5.3% per year). Conclusions and Relevance: In this cohort study of 849 adolescents, plasma biomarkers differed between uninjured participants and those with concussions, supporting their continued use to understand concussion neuropathology. Age and sex are critical considerations as these biomarkers progress toward clinical validation.

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)0.002
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 score0.249
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.041
GPT teacher head0.346
Teacher spread0.304 · 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".

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Citations20
Published2024
Admission routes3
Has abstractyes

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