A systematic review, meta-analysis <i>and</i> meta-regression amalgamating the driven approaches used <i>to</i> quantify dynamic cerebral autoregulation
Bibliographic record
Abstract
Numerous driven techniques have been utilized to assess dynamic cerebral autoregulation (dCA) in healthy and clinical populations. The current review aimed to amalgamate this literature and provide recommendations to create greater standardization for future research. The PubMed database was searched with inclusion criteria consisting of original research articles using driven dCA assessments in humans. Risk of bias were completed using Scottish Intercollegiate Guidelines Network and Methodological Index for Non-Randomized Studies. Meta-analyses were conducted for coherence, phase, and gain metrics at 0.05 and 0.10 Hz using deep-breathing, oscillatory lower body negative pressure (OLBNP), sit-to-stand maneuvers, and squat-stand maneuvers. A total of 113 studies were included, with 40 of these incorporating clinical populations. A total of 4126 participants were identified, with younger adults (18-40 years) being the most studied population. The most common techniques were squat-stands (n = 43), deep-breathing (n = 25), OLBNP (n = 20), and sit-to-stands (n = 16). Pooled coherence point estimates were: OLBNP 0.70 (95%CI:0.59-0.82), sit-to-stands 0.87 (95%CI:0.79-0.95), and squat-stands 0.98 (95%CI:0.98-0.99) at 0.05 Hz; and deep-breathing 0.90 (95%CI:0.81-0.99); OLBNP 0.67 (95%CI:0.44-0.90); and squat-stands 0.99 (95%CI:0.99-0.99) at 0.10 Hz. This review summarizes clinical findings, discusses the pros/cons of the 11 unique driven techniques included, and provides recommendations for future investigations into the unique physiological intricacies of dCA.
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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 | 0.031 | 0.089 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".