Dynamic cerebral autoregulation quantification with spontaneous arterial blood pressure oscillations: Is transfer function analysis our best option?
Bibliographic record
Abstract
Transfer function analysis (TFA) is a widely used analytical method to quantify dynamic cerebral autoregulation (dCA), which represents the ability of the cerebrovasculature to buffer transient changes in arterial blood pressure (ABP).This analytical approach estimates metrics reflecting the dynamic behaviour of dCA, assuming the latter can be represented as a linear control system.The variables computed from TFA are coherence {i.e., fraction of input signal [e.g., ABP] linearly related to output signal [e.g., cerebral blood velocity (CBv)]}, gain (i.e., CBv amplitude change for a given ABP change) and phase (i.e., difference in timing of ABP and CBv waveforms).Quantification of TFA for dCA can be completed using large transient-driven and spontaneous ABP oscillations (Claassen et al., 2016;Panerai et al., 2023).The use of spontaneous ABP fluctuations is appealing to physiologists and clinicians to assess dCA in diverse clinical contexts, where forced oscillations in ABP are not feasible or appear unsafe.Although acknowledged, it is now clearly documented that spontaneous TFA has a poor signal-to-noise ratio and greatly reduced reproducibility in comparison to forced oscillations (e.g., induced by repeated squat-stand manoeuvres).This considerably limits interpretation with respect to the linear association between ABP and CBv
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".