Dynamic cerebral autoregulation of the posterior cerebral artery after high level spinal cord injury
Bibliographic record
Abstract
It has been reported that those with high level (e.g., >; T 5 ) spinal cord injury (HLSCI) can tolerate profound hypotension in the absence of syncopal symptoms. Altered dynamic cerebral autoregulation (dCA) may help maintain cerebral blood flow (CBF) and improve tolerance to hypotension. In HLSCI, dCA of the posterior cerebral artery (PCA), which is relevant to syncopal episodes due to its supply arising from arteries perfusing the brainstem (i.e., critical region for maintaining consciousness), has not been investigated. Eight complete HLSCI and eight matched controls (AB) were examined in the upright position. Five‐minute long recordings of spontaneously occurring beat‐to‐beat changes in blood pressure (BP; photoplethysmography) as well as velocity in the middle cerebral artery (MCA) and PCA (transcranial Doppler) were recorded. To provide insight into dCA, transfer function analysis was performed to evaluate coherence, gain and phase of the MCA and PCA in the very low (0.02–0.07 Hz) and low (0.07–0.2 Hz) frequency ranges. In HLSCI, BP‐MCAv coherence was reduced by 33% in both the LF and VLF range, while VLF phase was reduced by 63% ( P < 0.05). Similarly, BP‐PCAv coherence was reduced by 48% in the LF and 54% in the VLF range ( P < 0.01). These results show that CBF regulation is altered in both the MCA and PCA in those with HLSCI.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".