Pooled blood volume measured by final flat-panel detector computed tomography predicts outcome after endovascular thrombectomy for acute ischemic stroke
Bibliographic record
Abstract
Background: Pooled blood volume (PBV), measured in real-time in the angiography room using an angiography system, correlates with cerebral blood volume (CBV). We examined the usefulness of PBV in endovascular thrombectomy (EVT) for acute ischemic stroke (AIS). Methods: EVT for AIS in the anterior circulation (internal carotid artery (ICA) and middle cerebral artery (MCA)) was performed in 31 cases (13 males, 18 females, average age 75.7 years). PBV was acquired using a biplane flat-panel detector (FD) angiographic system. Then, we measured the average PBV value in the M1-6 regions similar to the Alberta Stroke Program Early CT score (ASPECTS) before and after EVT. We investigated factors associated with favorable outcome at 90 days after EVT. Results: There were 13 patients (41.9%) in the good outcome group (mRS (modified Rankin Scale) ≦2) and 18 patients (58.1%) in the poor outcome group (mRS>2). In univariate analysis, NIHSS (National Institutes of Health Stroke Scale) (odds ratio [OR] 0.74, 95% CI 0.57-0.87, p < 0.0001) and post PBV value (odds ratio [OR] 1.13, 95% CI 1.03-1.29, p = 0.0086) were significantly associated with good outcome. The good outcome group had significantly higher post-thrombectomy PBV value (3.69 ± 0.32 ml/100 g versus 2.78 ± 0.93 ml/100 g, P = 0.002) compared to that of the poor outcome group. The relationship between pre-thrombectomy PBV value and outcome at 90 days was not significant. Conclusions: Post-operative PBV value measured by FD-CT (computed tomography) correlated with 90-day outcome after EVT for AIS. FD-CT-PBV would be one of the good predictors of clinical outcome.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".