MétaCan
Menu
Back to cohort
Record W4415469799 · doi:10.1177/08977151251387163

Relationship Between Exercise Tolerance and Event-Related Potentials on Recovery in Adults with Postconcussion Syndrome

2025· article· en· W4415469799 on OpenAlexafffund
Nicholas Moser, Miloš R. Popović, Sukhvinder Kalsi‐Ryan

Bibliographic record

VenueJournal of Neurotrauma · 2025
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
FundersMitacs
KeywordsRivermead post-concussion symptoms questionnaireConcussionHeart rateRehabilitationCognitionTraumatic brain injuryPost-concussion syndromeAerobic exerciseYoung adult

Abstract

fetched live from OpenAlex

From the diagnosis and management through to determining recovery, the clinical pathway for concussions and postconcussion syndrome (PCS) is reliant on symptom reporting. Under-reporting or over-reporting bias necessitates the need for more objective measures. Exercise intolerance has shown to be a strong predictor of adolescent concussion patients likely to have protracted recoveries. Its role in predicting outcomes for adults is less clear. In addition to physiological measures, event-related potentials (ERPs) have demonstrated altered cognitive processing across the concussion recovery stages in various demographics. The aim of the present study was to assess the relationship between baseline exercise tolerance and ERPs on the degree of improvement in symptoms postrehabilitation in adults with persistent postconcussion symptoms (PPCS). Forty participants (mean age ± SD, 39 ± 13.5 years) with PPCS (mean duration ± SD, 5 ± 3 months) took part in this 6-week clinical trial. Participants were randomized at baseline to a customized rehabilitation (CR) program or standard, symptom-based care (SC). At baseline, participants underwent a standard exam inclusive of an exercise tolerance test and completed a quantitative electroencephalogram to examine three auditory ERPs (N100 for sensory processing, P300 for attention, and N400 for cognitive processing). To examine the association of exercise tolerance and ERPs on recovery, linear regression was done to compare participants’ pre-post Rivermead Postconcussion Questionnaire scores (RPQ) with baseline delta heart rate (ΔHR), heart rate thresholds (HRt), and ERPs (amplitude and latency). The CR group showed significant and clinically meaningful improvements in reported symptoms (RPQ-3 and RPQ-13) and exercise tolerance (ΔHR, HRt). Notably, no baseline variables predicted outcomes in the CR group. Conversely, the SC group experienced no clinically meaningful symptom changes. For this group, baseline measures significantly correlated with symptom improvement. Exercise tolerance: Lower baseline ΔHR and HRt significantly correlated with less RPQ-3 (ΔHR: p = 0.01, R 2 = 0.28, coefficient = 0.03 || HRt: p = 0.006, R 2 = 0.36, coefficient = 0.04) and RPQ-13 improvement (ΔHR: p = 0.03, R 2 = 0.24, coefficient = 0.15 || HRt: p = 0.02, R 2 = 0.28, coefficient = 0.19). ERPs: Reduced N400 amplitude correlated with less improvement in RPQ-3 (RPQ-3: p = 0.05, R 2 = 0.19, coefficient = 0.55). Baseline exercise tolerance and ERPs were significant prognostic indicators for symptom improvement in adults with PPCS undergoing standard, symptom-based care. Participants with lower baseline exercise tolerance showed less improvement in postconcussion symptoms (RPQ-3 and RPQ-13), while reduced N400 amplitude was associated with poorer symptom outcomes. These baseline measures did not predict outcomes for patients receiving the customized rehabilitation program, suggesting that a comprehensive program may overcome initial physiological and cognitive vulnerabilities, leading to more robust recovery regardless of baseline presentation.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.063
GPT teacher head0.353
Teacher spread0.290 · 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 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
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of NeurotraumaSame topicTraumatic Brain Injury ResearchFrench-language works237,207