Global White Matter Hypoperfusion on <scp>CT</scp> Predicts Larger Infarcts and Hemorrhagic Transformation after Acute Ischemia
Bibliographic record
Abstract
INTRODUCTION: Presence of white matter hyperintensity (WMH) on MRI is a marker of cerebral small vessel disease and is associated with increased small vessel stroke and increased risk of hemorrhagic transformation (HT) after thrombolysis. AIM: We sought to determine whether white matter hypoperfusion (WMHP) on perfusion CT (CTP) was related to WMH, and if WMHP predisposed to acute lacunar stroke subtype and HT after thrombolysis. METHODS: Acute ischemic stroke patients within 12 h of symptom onset at 2 centers were prospectively recruited between 2011 and 2013 for the International Stroke Perfusion Imaging Registry. Participants routinely underwent baseline CT imaging, including CTP, and follow-up imaging with MRI at 24 h. RESULTS: Of 229 ischemic stroke patients, 108 were Caucasians and 121 Chinese. In the contralateral white matter, patients with acute lacunar stroke had lower cerebral blood flow (CBF) and cerebral blood volume (CBV), compared to those with other stroke subtypes (P = 0.041). There were 46 patients with HT, and WMHP was associated with increased risk of HT (R(2) = 0.417, P = 0.002). Compared to previously reported predictors of HT, WMHP performed better than infarct core volume (R(2) = 0.341, P = 0.034), very low CBV volume (R(2) = 0.249, P = 0.026), and severely delayed perfusion (Tmax>14 second R(2) = 0.372, P = 0.011). Patients with WMHP also had larger acute infarcts and increased infarct growth compared to those without WMHP (mean 28 mL vs. 13 mL P < 0.001). CONCLUSION: White matter hypoperfusion remote to the acutely ischemic region on CTP is a marker of small vessel disease and was associated with increased HT, larger acute infarct cores, and greater infarct growth.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".