Association between Autonomic Nervous System Function and Outcome Following Pediatric Concussion
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".