Matrix Metalloproteinase-9 and Monocyte Chemoattractant Protein-1 Are Associated With Collateral Status in Acute Ischemic Stroke With Large Vessel Occlusion
Bibliographic record
Abstract
Background and Purpose: In ischemic stroke, inflammatory status may condition the development of collateral circulation. Here we assessed the relationship between systemic inflammatory biomarkers and collateral status in large vessel occlusion before mechanical thrombectomy. Methods: HIBISCUS-STROKE is a cohort study including acute ischemic stroke patients with large vessel occlusion treated with mechanical thrombectomy following admission magnetic resonance imaging. MMP-9 (matrix metalloproteinase-9) and MCP-1 (monocyte chemoattractant protein-1) were measured on blood sampling collected at admission. Collateral status was assessed on pretreatment Digital subtraction angiography and categorized into poor (Higashida score, 0–2) and good (Higashida score, 3–4). A multiple logistic regression model was performed to detect independent markers of good collateral status. Results: One hundred and twenty-two patients were included, of them 71 patients (58.2%) had a good collateral status. In univariate analysis, low MMP-9 levels ( P =0.01), high MCP-1 levels ( P <0.01), a low National Institute of Health Stroke Score ( P =0.046), a high diastolic blood pressure ( P =0.049), the absence of tandem occlusion ( P =0.046), a high Alberta Stroke Program Early CT Score ( P <0.01) and a low volume on the diffusion-weighted imaging ( P <0.01) were associated with good collateral status. Following multivariate analysis, low MMP-9 levels ( P =0.02) and high MCP-1 levels ( P <0.01) remained associated with good collateral status. Conclusions: Low MMP-9 and high MCP-1 levels were associated with good pretreatment collateral status in patients with acute ischemic stroke with large vessel occlusion. These results might suggest a relationship between collateral status and inflammation.
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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 | 0.002 |
| 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 | 0.000 |
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".