3.14 Examining 24-hour heart rate variability during the acute and sub-acute phases of recovery following concussion
Bibliographic record
Abstract
Objective 1) Measure 24-hour heart rate variability (HRV) following sport-related concussion (SRC) and compare perturbations to athletes with musculoskeletal (MSK) injuries and healthy (CTL) athletes. 2) Examine the relationship between HRV measures and clinical symptoms post-injury. Design Prospective observational cohort study. Setting University. Participants 195 Canadian interuniversity athletes enrolled in the study and 147 athletes were included in the study – (CTL n=59 [female n=32, male n=27], SRC n=55 [female n=28, male n=27], MSK n=33 [female n=15, male n=19]). Interventions (or Assessment of Risk Factors) Symptom evaluation and 24-hour HRV recording were captured for CTL athletes at baseline, while SRC and MSK injury athletes completed the assessment at two timepoints: within 7 days of injury and at 1-month post-injury. Independent variables: presence or absence of SRC or MSK injury. Outcome Measures Dependent variables: HRV measures (mean and quantiles of RR vector, RMSSD, VLF, LF and HF power and LF/HF ratio) and clinical symptoms. Main Results No significant between-group differences were observed at both time points. Within-group analyses revealed SRC athletes displayed significantly lower values of HRV measures at 1-month post-injury when compared to the acute time point. Additionally, for the SRC group, there were significant correlations between all four symptom clusters and HRV measures: somatic (p<0.001), cognitive (p<0.001), emotional (p<0.001), and fatigue (p< 0.001), however for the MSK injury group, only the cognitive (p< 0.001) symptom cluster was significant. Conclusions These findings suggest that although there were no significant between-group differences, it appears that the two injury groups had differing HRV changes over clinical recovery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".