Thrombus Characteristics Are Related to Collaterals and Angioarchitecture in Acute Stroke
Bibliographic record
Abstract
BACKGROUND: We have theorized that clots with stasis are longer. We therefore explored the relationship between thrombus imaging characteristics on noncontrast computed tomography (NCCT) and magnetic resonance imaging (MRI) with clot length and pial collaterals on baseline computed tomography angiography (CTA). METHODS: Prospective study of acute ischemic stroke patients (2005-2009) from Keimyung University. Patients with known stroke symptom onset time, baseline CTA, MRI, and with M1-Middle Cerebral Artery (MCA)±intracranial internal carotid artery (ICA) occlusions were included. Clot length and pial collaterals were measured on baseline CTA. RESULTS: A total of 104 patients (mean age 65.1±12.28 years, 56.7% male, median baseline National Institutes of Health Stroke Scale 13) with intracranial ICA + MCA (n=50) or isolated M1-MCA (n=54) occlusions were included. Hyperdense sign on NCCT had a median clot length of 42.3 mm versus 29.5 mm when hyperdense negative (p=0.02). Clots showing blooming artifact on gradient recall echo MRI had a median length of 39.1 mm versus 24.5 mm without blooming (p=0.005). Patients with poor baseline collaterals on CTA had longer clots than those with intermediate/good collaterals (median clot length 49.4 mm vs 34.9 mm vs 20.5 mm respectively, p<0.001). In censored logistic regression modeling, clot length was an independent predictor of hyperdense sign (p=0.05) and of the presence of blooming artifact (p=0.006). CONCLUSIONS: Clot length and baseline collateral status are independent predictors of clot hyperdensity on NCCT and blooming artifact on gradient recall echo. Longer clots are more likely to be hyperdense and to bloom more, probably because portions of these clots are freshly formed locally due to of stasis of blood around the original clot. This stasis could be because of poor collaterals and inefficient angio-architecture within the cerebral arterial tree.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".