Aggressive blood pressure reduction is not associated with decreased perfusion in leukoaraiosis regions in acute intracerebral hemorrhage patients
Bibliographic record
Abstract
Leukoaraiosis regions may be more vulnerable to decreases in cerebral perfusion. We aimed to assess perfusion in leukoaraiosis regions in acute intracerebral hemorrhage (ICH) patients. We tested the hypothesis that aggressive acute BP reduction in ICH patients is associated with hypoperfusion in areas of leukoaraiosis. In the ICH Acutely Decreasing Arterial Pressure Trial (ICH ADAPT), patients with ICH <24 hours duration were randomized to two systolic BP (SBP) target groups (<150 mmHg vs. <180 mmHg). Computed tomography perfusion (CTP) imaging was performed 2h post-randomization. Leukoaraiosis tissue volumes were planimetrically measured using semi-automated threshold techniques on the acute non-contrast CT. CTP source leukoaraiosis region-of-interest object maps were co-registered with CTP post-processed maps to assess cerebral perfusion in these areas. Seventy-one patients were included with a mean age of 69±11.4 years, 52 of whom had leukoaraiosis. The mean relative Tmax (rTmax) of leukoaraiotic tissue (2.3±2s) was prolonged compared to that of normal appearing white matter in patients without leukoaraiosis (1.1±1.2s, p = 0.04). In the 52 patients with leukoaraiosis, SBP in the aggressive treatment group (145±20.4 mmHg, n = 27) was significantly lower than that in the conservative group (159.9±13.1 mmHg, n = 25, p = 0.001) at the time of CTP. Despite this SBP difference, mean leukoaraiosis rTmax was similar in the two treatment groups (2.6±2.3 vs. 1.8±1.6 seconds, p = 0.3). Cerebral perfusion in tissue affected by leukoaraiosis is hypoperfused in acute ICH patients. Aggressive BP reduction does not appear to acutely aggravate cerebral hypoperfusion.
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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) | 0.001 | 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".