Abstract 153: The Preserve Trial: Intensive Blood Pressure Lowering and Cerebral Blood Flow in Small Vessel Disease
Bibliographic record
Abstract
Background and Aims: In patients with severe cerebral small vessel disease (SVD), in whom both cerebral blood flow and cerebral autoregulation are reduced, intensive BP lowering might reduce cerebral blood flow (CBF) and increase the rate of white matter hyperintensity (WMH) progression. In an RCT we determined the effect of intensive BP lowering on CBF in lacunar stroke with confluent WMH. Method: In the PRESERVE trial Perfusion sub-study, patients from 2 sites with MRI confirmed symptomatic lacunar infarct and confluent WMH were randomised to “normal” (systolic=130-140mmHg, N=33) versus “intensive” (systolic=<125mmHg, N=29) BP targets. CBF was determined using arterial spin labelling; the primary end point was change in global CBF between baseline and 3 months. Linear regression was performed comparing change in CBF against change in BP. Analyses controlled for site. Results: Mean(SD) systolic BP reduced by 8(12) and 27(17)mmHg in the standard/intensive groups, respectively (difference between groups p <0.001) with achieved BP of 141(13) and 126(10) mmHg respectively. Baseline global CBF was 32(10) and 31(10)ml/min/100g in the standard/intensive groups. There was no difference in change in global CBF between treatment arms: standard, mean(SD) (ml/min/100g)= -0.46 (9.39); intensive, 0.73 (8.62), p =0.63. No differences were observed when analysis examined grey or white matter only, or was confined to those achieving target BP. Change in CBF did not have a significant association with change in systolic or diastolic BP. Conclusion: Intensive BP lowering did not reduce CBF in severe SVD characterised by lacunar stroke and confluent WMH. This suggests intensive CBF reduction is unlikely to accelerate WMH progression.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".