RAPID TILT FOR ASSESSMENT OF CEREBRAL BLOOD FLOW AUTOREGULATION
Bibliographic record
Abstract
The thigh cuff method has been used to quickly reduce cerebral perfusion pressure (CPP) and assess cerebral autoregulation. This technique can be uncomfortable and carries significant clinical risk. An alternative method to assess cerebral autoregulation is proposed here that manipulates CPP through a rapid head-up tilt (RHUT) protocol. Ten subjects, (5 male, 5 female) participated in tests which were performed in normocapnia (NORMO), hypocapnia (HYPO), and hypercapnia (HYPER). In each test, middle cerebral artery blood flow velocity (CBV; TCD), heart rate (HR), arterial blood pressure corrected for eye level (BPeye; Finapres), and end tidal CO2 (PETCO2) were measured continuously. The rate of regulation (RoR) was calculated for each RHUT at each level of CO2 representing the rate of change of cerebrovascular resistance (CVR) (index's−1) required to achieve a new steady state CBV for a given change in blood pressure. Compared to NORMO (64 ± 4.3cm's−1), baseline CBV increased in HYPER (88 ± 5.8 cm's−1) and decreased in HYPO (50 ± 4.3 cm's−1 (P < 0.002). RHUT caused CBV to decline in HYPER (P < 0.02) but not in NORMO or HYPO. RoR was significantly lower in HYPER (0.16 ± 0.05s−1)(P < 0.002) compared to NORMO (0.32 ± 0.04s−1) and HYPO (0.38 ± 0.08s−1) (P < 0.002) compared to NORMO (0.32 ± 0.04s−1) and HYPO (0.38 ± 0.08s−1) which were not different, indicating the expected slower autoregulation during hypercapnia. These results suggest that rapid head-up tilting may be a useful and convenient procedure to assess the rate of cerebral autoregulation.
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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) | 0.001 | 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) | 0.003 | 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".