Abstract 2835: Hyperdense MCA as a Predictor of Stroke Severity: Does Size Matter?
Bibliographic record
Abstract
Background and Objective Hyperdense MCA (HDMCA) sign is an early sign seen on CT head after acute ischemic stroke indicating acute arterial thrombosis. It has been reported as a predictor of stroke morbidity and mortality. The relationship of size of hyperdense middle cerebral artery (MCA) sign with stroke volume and functional outcome has not been sufficiently studied. Methods We did a retrospective analysis of prospectively collected data from 206 cases of acute ischemic stroke eligible for IV rt-PA by time criteria, admitted to Mayo Clinic from March, 2002 through June, 2011. Admission CT Head was studied to identify HDMCA sign which was further subcategorized according to size. CT head at 24 hours after intravenous tissue plasminogen activator was used to calculate Alberta Stroke Program Early CT Score (ASPECTS) to measure ischemic damage area. Functional outcome was assessed using the modified Rankin score at 3 months and dichotomized into less than equal to 2 (good outcome) and more than 2 (bad outcome). Univariate analysis including t-test, Chi-square, and Fisher Exact test was used when appropriate. Results Mean admission NIHSS score was 11±7. HDMCA sign was seen in 99 (48%) patients: 100% MCA occlusion (7) 76-100% M1 (17), 50-75% M1 (17), less than 50% M1 (6), MCA dot sign (23), sylvian dot sign (21) and sylvian MCA sign, defined as any sylvian MCA occlusion bigger than a dot (8). Univariate analysis showed statistically significant associations between our HDMCA classification and ASPECTS at 24 hours (p<0.001) and modified Rankin score at 3 months (p<0.001). Smaller infarctions (ASPECTS >8) were observed more often with lower MCA clot burden; MCA dot (48%), less than 50% MCA (80%), sylvian dot (57%) and sylvian MCA (38%), 50-75% M1 (6%), 75-100% M1 (0%) and 100% MCA (0%). Good outcome (mRS≤2 at 3 months) was seen more often with lower MCA clot burden; MCA dot (52%), less than 50% MCA (67%), sylvian dot (67%) and sylvian MCA (63%). Conversely, larger HDMCA size was associated with poor outcome (mRS>2); 50-75% M1 (100%), 75-100% (88%) and 100% MCA (100%). Conclusion The size of the HDMCA sign size is an important predictor of ischemic stroke mortality and morbidity. HDMCA should be measured upon admission as it may help refine prognostication and identify patients who could benefit from endovascular rescue therapy after intravenous thrombolysis.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".