Infarction Distribution Pattern in Acute Stroke May Predict the Extent of Leptomeningeal Collaterals
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate whether the distribution pattern of early ischemic changes in the initial MRI allows a practical method for estimating leptomeningeal collateralization in acute ischemic stroke (AIS). METHODS: Seventy-four patients with AIS underwent MRI followed by conventional angiogram and mechanical thrombectomy. Diffusion restriction in Diffusion weighted imaging (DWI) and correlated T2-hyperintensity of the infarct were retrospectively analyzed and subdivided in accordance with Alberta Stroke Program Early CT score (ASPECTS). Patients were angiographically graded in collateralization groups according to the method of Higashida, and dichotomized in 2 groups: 29 subjects with collateralization grade 3 or 4 (well-collateralized group) and 45 subjects with grade 1 or 2 (poorly-collateralized group). Individual ASPECTS areas were compared among the groups. RESULTS: Means for overall DWI-ASPECTS were 6.34 vs. 4.51 (well vs. poorly collateralized groups respectively), and for T2-ASPECTS 9.34 vs 8.96. A significant difference between groups was found for DWI-ASPECTS (p<0.001), but not for T2-ASPECTS (p = 0.088). Regarding the individual areas, only insula, M1-M4 and M6 showed significantly fewer infarctions in the well-collateralized group (p-values <0.001 to 0.015). 89% of patients in the well-collateralized group showed 0-2 infarctions in these six areas (44.8% with 0 infarctions), while 59.9% patients of the poor-collateralized group showed 3-6 infarctions. CONCLUSION: Patients with poor leptomeningeal collateralization show more infarcts on the initial MRI, particularly in the ASPECTS areas M1 to M4, M6 and insula. Therefore DWI abnormalities in these areas may be a surrogate marker for poor leptomeningeal collaterals and may be useful for estimation of the collateral status in routine clinical evaluation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".