Wavelet decomposition analysis is a clinically relevant strategy to evaluate cerebrovascular buffering of blood pressure after spinal cord injury
Bibliographic record
Abstract
The capacity of the cerebrovasculature to buffer changes in blood pressure (BP) is crucial to prevent stroke, the incidence of which is three- to fourfold elevated after spinal cord injury (SCI). Disruption of descending sympathetic pathways within the spinal cord due to cervical SCI may result in impaired cerebrovascular buffering. Only linear analyses of cerebrovascular buffering of BP, such as transfer function, have been used in SCI research. This approach does not account for inherent nonlinearity and nonstationarity components of cerebrovascular regulation, often depends on perturbations of BP to increase the statistical power, and does not account for the influence of arterial CO2 tension. Here, we used a nonlinear and nonstationary analysis approach termed wavelet decomposition analysis (WDA), which recently identified novel sympathetic influences on cerebrovascular buffering of BP occurring in the ultra-low-frequency range (ULF; 0.02–0.03Hz). WDA does not require BP perturbations and can account for influences of CO2 tension. Supine resting beat-by-beat BP (Finometer), middle cerebral artery blood velocity (transcranial Doppler), and end-tidal CO2 tension were recorded in cervical SCI ( n = 14) and uninjured ( n = 16) individuals. WDA revealed that cerebral blood flow more closely follows changes in BP in the ULF range ( P = 0.0021, Cohen’s d = 0.89), which may be interpreted as an impairment in cerebrovascular buffering of BP. This persisted after accounting for CO2. Transfer function metrics were not different in the ULF range, but phase was reduced at 0.07–0.2 Hz ( P = 0.03, Cohen’s d = 0.31). Sympathetically mediated cerebrovascular buffering of BP is impaired after SCI, and WDA is a powerful strategy for evaluating cerebrovascular buffering in clinical populations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".