Assessment of Collateral Flow with Multi-Phasic CT: Correlation with Diffusion Weighted MRI in MCA Occlusion
Bibliographic record
Abstract
PURPOSE: To correlate collateral flow on multiphasic contrast enhancement computed tomography (CT) and graded ischemic changes on diffusion weighted MR in patients with acute middle cerebral artery (MCA) infarction. MATERIALS AND METHODS: A retrospective evaluation of diffusion weighted images (DWIs) and three phasic contrast enhanced CT (CECT) was performed on 11 patients with MCA occlusions. The area of ischemic change on DWIs was graded according to the Alberta Stroke Program Early CT Score (ASPECTS) criteria. To evaluate collateral flow on three phasic CECT, we counted the number of contrast enhancing MCA branches distal to the occlusion site at the sylvian fissure from predetermined axial images. The collateral ratios of counted numbers to those at the normal side were calculated at each phase (CR1, CR2, CR3). We then compared collateral ratios from the three phasic CECT with ASPECTS data from DWIs. RESULTS: Collateral ratios from the three phasic CECT were determined to be CR1 .48 ± .27, CR2 .73 ± .36 and CR3 .72 ± .30. We discovered a correlation between both the CR2 and ASPECTS (r= .675, P= .023) and the CR3 and ASPECTS (r= .664, P= .026). CONCLUSION: The number of contrast enhancing branches distal to the MCA occlusion, as counted in the sylvian fissure on later phase images of multiphasic CECT, reflects the status of collateral flow, and correlates with ASPECTS on DWIs.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".