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Association between Autonomic Nervous System Function and Outcome Following Pediatric Concussion

2019· article· en· W2977806503 on OpenAlexfundno aff
Colt A. Coffman, Jacob Kay, Kathryn Saba, Jeffery Holloway, Robert Davis Moore

Bibliographic record

VenueNeurology · 2019
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsConcussionHeart rate variabilityRivermead post-concussion symptoms questionnaireMedicineAutonomic nervous systemAssociation (psychology)CognitionPhysical therapyCardiologyInternal medicinePhysical medicine and rehabilitationHeart rateAudiologyPoison controlInjury preventionPsychologyBlood pressureRehabilitationPsychiatryEmergency medicine

Abstract

fetched live from OpenAlex

Objective To examine the association between heart rate variability (HRV) and pediatric concussion outcomes. We hypothesized that HRV would be related to both clinical symptoms and cognition and that HRV parameters at 2-weeks post-injury would predict outcomes at 5 weeks. Background Despite the growing prevalence of concussion among children, research focusing on an objective measure of recovery is lacking. Evidence suggests that dysregulation of the autonomic nervous system may be present following concussion. HRV, an objective measure of autonomic function, could prognosticate persistent symptoms following pediatric concussion. Design/Methods Forty-five concussed children were evaluated 2 weeks and 5 weeks post-injury. Clinical symptoms were evaluated using the Rivermead Post-Concussion Symptoms Questionnaire (RPQ). Cognition was assessed using a modified CogState Brain Injury Test Battery. Time-domain (SDNN, RMSSD, NN50) and frequency-domain (log-transformed values of low- and high-frequency power) variables of HRV were measured during a resting 5-minute recording. Key demographic and injury characteristics, as well as mean heart rate were factored as covariates. Results At 2 weeks post-injury, low-frequency measures showed a positive association with overall RPQ symptoms and Groton Maze Learning and Recall errors (p’s < 0.05). At 5 weeks post-injury, NN50 and low-frequency measures showed a positive association with Groton Maze Learning errors (p’s < 0.05). At both timepoints, time-domain and high-frequency measures showed a negative association with One-Back task accuracy (p’s < 0.05). Additionally, time-domain and high-frequency measures at 2 weeks post-injury predicted One-Back task accuracy at 5 weeks post-injury (p’s < 0.05). Conclusions Our findings support our hypothesis and indicate that HRV is associated with clinical symptoms and cognitive function post-injury. Importantly, our results also suggest that metrics of HRV collected in the acute phase may serve as a prognostic tool. Additional longitudinal research is warranted in order to replicate the current findings.

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)0.001
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.034
GPT teacher head0.301
Teacher spread0.267 · 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".

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Citations2
Published2019
Admission routes1
Has abstractyes

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