Assessing Heart Rate Variability As a Surrogate Measure of Cardiac Autonomic Function in Chronic Traumatic Spinal Cord Injury
Bibliographic record
Abstract
Background: Although cardiac autonomic dysfunction is a contributing factor for cardiovascular disease development in individuals with a spinal cord injury (SCI), it remains poorly understood. Heart rate variability (HRV) analysis has the potential to non-invasively assess the cardiac autonomic nervous system. The study objectives are (a) to determine if there are differences in HRV measures across neurological level of impairment (NLI) and American Spinal Cord Injury Association Impairment Scale (AIS) subgroups, and (b) to determine if there is a relationship between HRV frequency measures (low frequency [LF] and high frequency [HF]) at rest. Methods: We conducted a secondary data analysis of a primary data set from a published cross-sectional study of electrocardiogram recordings of 56 subjects (44 men and 12 women, mean age ± SD = 46.75 ± 12.44 years) with a chronic traumatic SCI (C1-T12, AIS A-D, ≥2 years post injury). HRV was analyzed using time and frequency domain measures. Results: There were no significant HRV differences across NLI and AIS subgroups. The LF and HF indices were positively correlated in the entire sample (r = 0.708, p < .0001) and among impairment subgroups. Conclusion: No differences were observed in the HRV time and frequency measures when compared across NLI and AIS subgroups. The results were considered inconclusive, since possible explanations include inadequate sample size as well as other physiological considerations. A positive correlation was found between LF and HF when assessed at rest. The relationship between LF and HF may not necessarily represent a rebalanced autonomic nervous system, but it does question the utility of solely measuring LF:HF at rest in persons with chronic SCI.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".