Blood Pressure Variability in Daily Life: Comparing Individuals with Cervical and Upper-Thoracic Spinal Cord Injury to Non-Injured Controls
Bibliographic record
Abstract
Abstract Background Spinal cord injury (SCI) above the sixth thoracic spinal cord segment can disrupt autonomic cardiovascular control, leading to increased BP variability (BPV). This study compared BPV, nocturnal dipping patterns, and blood pressure (BP) fluctuations among individuals with cervical SCI (C-SCI), upper-thoracic SCI (UT-SCI), and non-injured controls (NI-C). Methods Using 24-hour ambulatory blood pressure monitoring, we analyzed BPV (via standard deviation [SD], coefficient of variation [CoV], average real variability [ARV], and variability independent of the mean [VIM]), nocturnal dipping, and systolic/diastolic BP fluctuations. Nocturnal dipping percentages were calculated and patterns classified. Hypotensive (SBP <100mmHg, DBP <70mmHg) and hypertensive (SBP >150mmHg per clinical guideline thresholds) events were identified. BP distribution was analyzed using skewness and kurtosis. Results Eighty participants (44 C-SCI, 19 UT-SCI, 17 NI-C) had 66 ± 18 measurements taken and are included in the analysis. The C-SCI group exhibited significantly higher systolic BPV metrics across SD, CoV, ARV, and VIM compared to the UT-SCI and NI-C groups ( P < 0.01). C-SCI nocturnal BP dips were reduced, and reverse dipping patterns were more prevalent ( P < 0.001). Hypotensive events occurred more frequently in C-SCI and UT-SCI compared to NI-C (both P < 0.001). SCI groups, particularly those with C-SCI, showed significantly more right-skewed SBP distributions and a higher proportion of non-Gaussian BP profiles, suggesting increased BP lability. Conclusion Individuals with C-SCI showed significantly increased BPV and impaired nocturnal dipping, while both C-SCI and UT-SCI demonstrated heightened susceptibility to hypotensive events. These findings highlight the need for targeted cardiovascular monitoring and interventions in individuals with C-SCI and UT-SCI to mitigate the known risks associated with BP dysregulation.
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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 | 0.000 | 0.001 |
| 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".