Regional Leptomeningeal Score on CT Angiography Predicts Clinical and Imaging Outcomes in Patients with Acute Anterior Circulation Occlusions
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The regional leptomeningeal score is a strong and reliable imaging predictor of good clinical outcomes in acute anterior circulation ischemic strokes and can therefore be used for imaging based patient selection. Efforts to determine biological determinants of collateral status are needed if techniques to alter collateral behavior and extend time windows are to succeed. MATERIALS AND METHODS: This was a retrospective Institutional Review Board-approved study of patients with acute ischemic stroke and M1 middle cerebral artery+/- intracranial internal carotid artery occlusion at our center from 2003 to 2009. The rLMC score is based on scoring pial and lenticulostriate arteries (0, no; 1, less; 2, equal or more prominent compared with matching region in opposite hemisphere) in 6 ASPECTS regions (M1-6) plus anterior cerebral artery region and basal ganglia. Pial arteries in the Sylvian sulcus are scored 0, 2, or 4. Good clinical outcome was defined as mRS ≤ 2 at 90 days. RESULTS: The analysis included 138 patients: 37.6% had a good (17-20), 40.5% a medium (11-16), and 21.7% a poor (0-10) rLMC score. Interrater reliability was high, with an intraclass correlation coefficient of 0.87 (95% CI, 0.77%-0.95%). On univariate analysis, no single vascular risk factor was associated with the presence of poor rLMCs (P ≥ .20 for all comparisons). In multivariable analysis, the rLMC score (good versus poor: OR, 16.7; 95% CI, 2.9%-97.4%; medium versus poor: OR, 9.2, 95% CI, 1.7%-50.6%), age (< 80 years), baseline ASPECTS (≥ 8), and clot burden score (≥ 8) were independent predictors of good clinical outcome. CONCLUSIONS: The rLMC score is a strong imaging parameter on CT angiography for predicting clinical outcomes in patients with acute ischemic strokes.
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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 | 0.005 |
| 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 | 0.000 |
| 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".