Assessment of Leptomeningeal Collaterals Using Dynamic CT Angiography in Patients with Acute Ischemic Stroke
Bibliographic record
Abstract
Whole-brain dynamic time-resolved computed tomography angiography (CTA) is a technique developed on the new 320-detector row CT scanner capable of generating time-resolved cerebral angiograms from skull base to vertex. Unlike a conventional cerebral angiogram, this technique visualizes pial arterial filling in all vascular territories, thereby providing additional hemodynamic information. Ours was a retrospective study of consecutive patients with ischemic stroke and M1 middle cerebral artery +/- intracranial internal carotid artery occlusions presenting to our center from June 2010 and undergoing dynamic time-resolved CTA and perfusion CT within 6 hours of symptom onset. Leptomeningeal collateral status was assessed by determining relative prominence of pial arteries in the ischemic region, rate and extent of retrograde flow, and various topographical patterns of pial arterial filling. Twenty-five patients were included in the study. We demonstrate the existence of the following novel properties of leptomeningeal collaterals in humans: (a) posterior (posterior cerebral artery (PCA)-MCA) dominant collateralization, (b) intra-territorial 'within MCA region' leptomeningeal collaterals, and (c) significant variability in size, extent, and retrograde filling time in pial arteries. We also describe a simple and reliable collateral grading template that, for the first time on dynamic CTA, incorporates back-filling time as well as size and extent of collateral filling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".