Role of Recanalization in Acute Stroke Outcome: Rationale for a CT Angiogram-Based “Benefit of Recanalization” Model: Fig 1.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In acute middle cerebral artery (MCA) stroke, CT angiographic (CTA) source images (CTA-SI) identify tissue likely to infarct despite early recanalization. This pilot study evaluated the impact of recanalization status on clinical and radiologic predictors of patient outcomes. MATERIALS AND METHODS: Of 44 patients undergoing CT/CTA within 6 hours of developing symptoms of proximal MCA ischemia, 19 patients achieved complete proximal MCA (MCA M1) recanalization. Admission National Institutes of Health Stroke Scale (NIHSS) score, onset-to-imaging time, CTA-SI Alberta Stroke Program Early CT Score, MCA M1 occlusion, cerebrovascular collaterals score, and CTA-SI lesion volume were correlated with 3- to 6-month follow-up modified Rankin Scale (mRS). We developed 2 stepwise regression models: one for patients with complete MCA M1 recanalization and one for patients without complete recanalization. RESULTS: Complete and incomplete recanalization groups had similar median admission NIHSS scores (19 versus 19) and mean onset-to-imaging times (2.3 versus 1.9 hours) but different proportions of patients achieving mRS scores 0-2 (74% versus 40%; P = .04). The only independent predictors of clinical outcome in patients with complete recanalization were onset-to-imaging time and admission CTA-SI lesion volume (total model R(2) = 0.75; P = .01). The only independent predictors of outcome in patients with incomplete recanalization were admission CTA-SI lesion volume and NIHSS score (total model R(2) = 0.66; P = .007). CONCLUSION: Regardless of recanalization status, admission CTA-SI lesion volume was associated with clinical outcome. Recanalization status did, however, affect which variables in addition to CTA-SI volume significantly impacted clinical outcome: time with complete recanalization and NIHSS with incomplete recanalization. This finding may support the development of a model predicting the potential clinical benefit expected with early successful recanalization.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".