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2.16 Blood biomarkers for sport-related concussion: considering the effects of interval exercise and matrix differences

2024· article· en· W4391384550 on OpenAlexaff
Linden C. Penner, Jason Tabor, Joel S. Burma, Heather Godfrey, Jean‐Michel Galarneau, Jennifer Cooper, Mohammad Ghodsi, Chantel T. Debert, Cheryl L. Wellington, Carolyn A. Emery, Jonathan D. Smirl

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicExplainable Artificial Intelligence (XAI)
Canadian institutionsAlberta Bone and Joint Health InstituteAlberta Children's HospitalInternational Collaboration On Repair DiscoveriesUniversity of British ColumbiaHotchkiss Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsMedicineInternal medicineConfidence intervalConcussionCrossover studyHigh-intensity interval trainingInterval trainingRandomized controlled trialBiomarkerPhysical therapyCardiologyPoison controlInjury preventionPathology

Abstract

fetched live from OpenAlex

Objective Examine plasma t-tau, NFL, and GFAP levels in serial samples following interval exercise, while considering age, sex, and sleep, and assess agreement between plasma and serum biomarker concentrations. Design Randomized-crossover cohort. Setting UCalgary Laboratory. Participants Ten healthy participants were recruited from the UCalgary community (ages 19–26; 7:3/female:male). Participants excluded if prior diagnosed concussion or metabolic disease, not recreationally active, and/or a daily smoker. Interventions (or Assessment of Risk Factors) Three differing intensity 30-minute interval exercise conditions on a cycle ergometer separated by one month in randomized order (control/rest, moderate intensity interval training (MIIT), high intensity interval training (HIIT)). Outcome Measures Plasma and serum concentrations of t-tau, NFL, and GFAP at serial timepoints (pre, during, and 0, 1, 2, 4, 6, 8, 24, 48 hours following). Main Results Compared to control, plasma NFL and GFAP decreased immediately following completion of MIIT (NFL;β=-1.002,95%CI:-1.852--0.152,p=0.021. GFAP;β=-14.750,95%CI:-27.154--2.345,p=0.020) and HIIT (NFL;β=-1.414,95%CI:-2.263--0.564,p=0.001. GFAP;β=-20.956,95%CI:-33.358--8.554,p=0.001). More sleep was associated with lower plasma NFL (β=-0.003,95%CI:-0.005--0.001,p=0.012). Bland-Altman plots showed 1.36 pg/mL (95%LOA:-0.77–3.49) greater t-tau in plasma than serum, and 1.54 pg/mL (95%LOA:-4.16–1.07) less NFL and 11.80 pg/mL (95%LOA:-43.46–19.86) less GFAP in plasma than serum. Conclusions Plasma t-tau appeared unaffected by MIIT and HIIT. Short-lived NFL and GFAP decreases along with poor agreement between plasma and serum concentrations suggests exercise influences and matrix differences are pertinent considerations for clinical validation of blood biomarkers associated with acute sport-related concussion. Future studies investigating influence of sleep are warranted.

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)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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 designBench or experimental
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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Citations0
Published2024
Admission routes1
Has abstractyes

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