Regional disparity in continuously measured time-domain cerebrovascular reactivity indices: a scoping review of human literature
Bibliographic record
Abstract
Abstract Objective: Cerebral blood vessels maintaining relatively constant cerebral blood flow (CBF) over wide range of systemic arterial blood pressure (ABP) is referred to as cerebral autoregulation (CA). Impairments in CA expose the brain to pressure-passive flow states leading to hypoperfusion and hyperperfusion. Cerebrovascular reactivity (CVR) metrics refer to surrogate metrics of pressure-based CA that evaluate the relationship between slow vasogenic fluctuations in cerebral perfusion pressure/ABP and a surrogate for pulsatile CBF/cerebral blood volume. Approach: We performed a systematically conducted scoping review of all available human literature examining the association between continuous CVR between more than one brain region/channel using the same CVR index. Main Results: In all the included 22 articles, only handful of transcranial doppler (TCD) and near-infrared spectroscopy (NIRS) based metrics were calculated for only two brain regions/channels. These metrics found no difference between left and right sides in healthy volunteer, cardiac surgery, and intracranial hemorrhage patient studies. In contrast, significant differences were reported in endarterectomy, and subarachnoid hemorrhage studies, while varying results were found regarding regional disparity in stroke, traumatic brain injury, and multiple population studies. Significance: Further research is required to evaluate regional disparity using NIRS-based indices and to understand if NIRS-based indices provide better regional disparity information than TCD-based indices.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".