Autoregulation of Cerebral Blood Flow is Preserved in Primary Intracerebral Hemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Treatment of acute hypertension after intracerebral hemorrhage (ICH) is controversial. In the context of disrupted cerebral autoregulation, blood pressure (BP) reduction may cause decreased cerebral blood flow (CBF). We used serial computed tomography perfusion to test the hypothesis that CBF remains stable after BP reduction. METHODS: Patients recruited within 72 hours of ICH were imaged with computed tomography perfusion before and after BP treatment. Change in perihematoma relative (r) CBF after BP treatment was the primary end point. RESULTS: Twenty patients were imaged with computed tomography perfusion at a median (interquartile range) time from onset of 20.2 (25.7) hours and reimaged 2.1 (0.5) hours later, after BP reduction. Mean systolic BP in treated patients (n=16; 4 untreated as BP<target at baseline) decreased significantly between the first (168 ± 21 mm Hg) and second (141 ± 19 mm Hg; P<0.0001) computed tomography perfusion scans. The primary end point of rCBF was not affected by BP reduction (pretreatment=0.89 ± 0.11; post-treatment=0.87 ± 0.11 mL/100 g per minute; P=0.37). Linear regression showed no relationship between changes in systolic BP and perihematoma rCBF (β=0.001 [-0.002 to 0.003]; P=0.63). CONCLUSIONS: CBF remained stable after acute BP reduction, suggesting some preservation 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) | 0.000 | 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".