Blood Pressure Reduction Does Not Reduce Perihematoma Oxygenation: A CT Perfusion Study
Bibliographic record
Abstract
Blood pressure (BP) reduction after intracerebral hemorrhage (ICH) is controversial, because of concerns that this may cause critical reductions in perihematoma perfusion and thereby precipitate tissue damage. We tested the hypothesis that BP reduction reduces perihematoma tissue oxygenation.Acute ICH patients were randomized to a systolic BP target of <150 or <180 mm Hg. Patients underwent CT perfusion (CTP) imaging 2 hours after randomization. Maps of cerebral blood flow (CBF), maximum oxygen extraction fraction (OEF(max)), and the resulting maximum cerebral metabolic rate of oxygen (CMRO2(max)) permitted by local hemodynamics, were calculated from raw CTP data.Sixty-five patients (median (interquartile range) age 70 (20)) were imaged at a median (interquartile range) time from onset to CTP of 9.8 (13.6) hours. Mean OEF(max) was elevated in the perihematoma region (0.44±0.12) relative to contralateral tissue (0.36±0.11; P<0.001). Perihematoma CMRO2(max) (3.40±1.67 mL/100 g per minute) was slightly lower relative to contralateral tissue (3.63±1.66 mL/100 g per minute; P=0.025). Despite a significant difference in systolic BP between the aggressive (140.5±18.7 mm Hg) and conservative (163.0±10.6 mm Hg; P<0.001) treatment groups, perihematoma CBF was unaffected (37.2±11.9 versus 35.8±9.6 mL/100 g per minute; P=0.307). Similarly, aggressive BP treatment did not affect perihematoma OEF(max) (0.43±0.12 versus 0.45±0.11; P=0.232) or CMRO2(max) (3.16±1.66 versus 3.68±1.85 mL/100 g per minute; P=0.857). Blood pressure reduction does not affect perihematoma oxygen delivery. These data support the safety of early aggressive BP treatment in ICH.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".