CT Perfusion Quantification of Small-Vessel Ischemic Severity
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cerebral blood flow (CBF) abnormalities are previously demonstrated in white matter disease. A gradation of change may exist between patients with mild and more severe white matter disease. An association between blood brain barrier dysfunction, increasing age and white matter disease is also suggested. The purpose of this study was to quantify and correlate white matter disease severity and CT perfusion (CTP)-derived CBF and to determine whether permeability surface abnormality increases with white matter disease severity. MATERIALS AND METHODS: One hundred twenty patients with strokelike symptoms underwent CTP and MR imaging. Of these, 35 patients (15 women, 20 men; age, 66 +/- 15.7 years) with rapidly resolving symptoms and normal imaging characteristics consistent with transient ischemic attack were retrospectively reviewed and constituted the study cohort. Two blinded neurologists rated white matter severity, assigning age-related white matter change (ARWMC) scores. Patients were dichotomized a priori into mild and moderate-to-severe. CBF, cerebral blood volume (CBV), mean transit time (MTT), and permeability surface product maps were calculated for periventricular and subcortical white matter regions and average white and gray matter. Associations with white matter severity were tested by uni- and multivariate logistic regression analyses. Receiver operating characteristic analysis was performed. RESULTS: White matter disease was mild in 26 patients and moderate-to-severe in 9. Age was associated with increased likelihood of having moderate-to-severe white matter disease (P = .02). ARWMC correlated with subcortical (r = -0.50, P < .001) and average CBF (r = -0.55, P < .001). White matter severity was associated with subcortical (P = .03) and average (P = .03) white matter CBF, with a trend toward periventricular white matter CBF (P = .05). Uni- and multivariate analysis controlling for the confounding effect of age demonstrated significant association between white matter severity and subcortical (P = .032) white matter CBF. Area under the curve was 0.82. No permeability surface abnormality was found. CONCLUSIONS: CTP-derived subcortical white matter CBF is independently associated with white matter disease severity.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".