Heart Rate Variability and Mild Traumatic Brain Injury: Case Study and Review of Literature
Bibliographic record
Abstract
<h3>Objective</h3> Use biofeedback measures to manage a patient9s long term recovery from concussion. <h3>Background</h3> Sports-related mild traumatic brain injury (MTBI) is estimated to affect 3.8 million people in the United States. Identifying quantitative measures of recovery has become a point of interest in treatment. Heart Rate Variability (HRV), the average fluctuation in the interval between heartbeats, shows promise as a noninvasive biomarker. <h3>Design/Methods</h3> Case report following cardiovascular recovery of a 15 year old cross country runner 4 months post-injury. Average heart rate and maximum heart rate per training session were collected from the patient9s smart device. <h3>Results</h3> A 15-year-old Caucasian male cross-country runner hit the back of his head during a soccer game suffering an MTBI. The patient rested from the activity for 1 week then returned to training. Two months after the injury the patient complained of persistent shortness of breath, fatigue, and increased heart rate while running. According to the patient, his average BPM while running prior to the injury was in the 160s. The patient9s smart device post-concussion reports a spike into the 180s. 3 months post-concussion the patient was instructed to keep his heart rate below 170 during training. In the following month, the patient9s condition improved gradually with a return to baseline activity. <h3>Conclusions</h3> HRV is a promising point of investigation for the management of post-concussive symptoms. Further research is necessary to elucidate the long term effects of concussion on heart rate variability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".