Non-Invasive and Minimally-Invasive Cerebral Autoregulation Assessment: A Narrative Review of Techniques and Implications for Clinical Research
Bibliographic record
Abstract
The process of cerebral vessels regulating constant cerebral blood flow over a wide range of systemic arterial pressures is termed cerebral autoregulation (CA). Static and dynamic autoregulation are two types of CA measurement techniques, with the main difference between these measures relating to the time scale used. Static autoregulation looks at the long-term change in blood pressures, while dynamic autoregulation looks at the immediate change. Techniques that provide regularly updating measures are referred to as continuous, whereas intermittent techniques take a single at point in time. However, a technique being continuous or intermittent is not implied by if the technique measures autoregulation statically or dynamically. This narrative review outlines technical aspects of non-invasive and minimally-invasive modalities along with providing details on the non-invasive and minimally-invasive measurement techniques used for CA assessment. These non-invasive techniques include neuroimaging methods, transcranial Doppler, and near-infrared spectroscopy while the minimally-invasive techniques include positron emission tomography along with magnetic resonance imaging and radiography methods. Further, the advantages and limitations are discussed along with how these methods are used to assess CA. At the end, the clinical considerations regarding these various techniques are highlighted.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".