Nonstationary multivariate modeling of cerebral autoregulation during resting state and hypercapnia (1184.10)
Bibliographic record
Abstract
We examined the time‐varying characteristics of cerebral autoregulation and hemodynamics during resting state and hypercapnia by using recursively estimated multivariate (two‐input) models which quantify the dynamic effects of mean arterial blood pressure (ABP) and end‐tidal CO2 tension (PETCO2) on middle cerebral artery blood flow velocity (CBFV). Experimental measurements of spontaneous variations of these signals were obtained from thirteen healthy subjects under normal, free‐breathing conditions. Beat‐to‐beat values of ABP and CBFV, as well as breath‐to‐breath values of PETCO2 were also obtained in 8 female subjects during baseline and sustained euoxic hypercapnia. The multiple‐input, single‐output linear models used to describe the relationship between ABP, PETCO2 and CBFV were based on the Laguerre expansion technique. In order to account for the different dynamics associated with each input, the model parameters were updated using a recursive least squares scheme with constant and adaptive multiple forgetting factors. The results reveal the presence of nonstationarities that are more pronounced in the very low frequency range. By comparing one‐input (MABP) and two‐input (MABP and PETCO2) models, our results point out that the incorporation of PETCO2 as an additional input yields less time‐varying estimates of dynamic pressure autoregulation obtained from single‐input (MABP‐CBFV) models, suggesting the important role of PETCO2 and the possible shortcomings of assessing dynamic autoregulation using such models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".