Reduced Blood Flow in Normal White Matter Predicts Development of Leukoaraiosis
Bibliographic record
Abstract
The purpose of this study was to investigate whether low cerebral blood flow (CBF) is associated with subsequent development of white matter hyperintensities (WMH). Patients were included from a longitudinal magnetic resonance (MR) imaging study of minor stroke/transient ischemic attack patients. Images were co-registered and new WMH at 18 months were identified by comparing follow-up imaging with baseline fluid-attenuated inversion recovery (FLAIR). Regions-of-interest (ROIs) were placed on FLAIR images in one of three categories: (1) WMH seen at both baseline and follow-up imaging, (2) new WMH seen only on follow-up imaging, and (3) regions of normal-appearing white matter at both time points. Registered CBF maps at baseline were used to measure CBF in the ROIs. A multivariable model was developed using mixed-effects logistic regression to determine the effect of baseline CBF on the development on new WMH. Forty patients were included. Mean age was 61±11 years, 30% were female. Low baseline CBF, female sex, and presence of diabetes were independently associated with the presence of new WMH on follow-up imaging. The odds of having new WMH on follow-up imaging reduces by 0.61 (95% confidence interval=0.57 to 0.65) for each 1 mL/100 g per minute increase in baseline CBF. We conclude that regions of white matter with low CBF develop new WMH on follow-up imaging.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".