Characterizing the Severity of Autonomic Cardiovascular Dysfunction after Spinal Cord Injury Using a Novel 24 Hour Ambulatory Blood Pressure Analysis Software
Bibliographic record
Abstract
Cardiovascular disease is one of the leading causes of morbidity and mortality in the spinal cord injury (SCI) population. SCI may disrupt autonomic cardiovascular homeostasis, which can lead to persistent hypotension, irregular diurnal rhythmicity, and the development of autonomic dysreflexia (AD). There is currently no software available to perform automated detection and evaluation of cardiovascular autonomic dysfunction(s) such as those generated from 24 h ambulatory blood pressure monitoring (ABPM) recordings in the clinical setting. The objective of this study is to compare the efficacy of a novel 24 h ABPM Autonomic Dysfunction Detection Software against manual detection and to use the software to demonstrate the relationships between level of injury and the degree of autonomic cardiovascular impairment in a large cohort of individuals with SCI. A total of 46 individuals with cervical (group 1, n = 37) or high thoracic (group 2, n = 9) SCI participated in the study. Outcome measures included the frequency and severity of AD, frequency of hypotensive events, and diurnal variations in blood pressure and heart rate. There was good agreement between the software and manual detection of AD events (Bland-Altman limits of agreement = ±1.458 events). Cervical SCI presented with more frequent (p = 0.0043) and severe AD (p = 0.0343) than did high thoracic SCI. Cervical SCI exhibited higher systolic and diastolic blood pressure during the night and lower heart rate during the day than high thoracic SCI. In conclusion, our ABPM AD Detection Software was equally as effective in detecting the frequency and severity of AD and hypotensive events as manual detection, suggesting that this software can be used in the clinical setting to expedite ABPM analyses.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".