Predicting Futile Recanalization After Endovascular Thrombectomy for Patients With Stroke With Large Cores: The SNAP Score
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: We aimed to develop and validate a prediction score for futile recanalization (FR) for large vessel occlusion (LVO) presenting low Alberta Stroke Program Early Computed Tomography Score (ASPECTS) for patients who underwent endovascular thrombectomy (EVT). METHODS: Patients with anterior circulation LVO with low ASPECTS (<6) who underwent successful EVT (modified treatment in cerebral ischemia score ≥2b) from Stroke Thrombectomy and Aneurysm Registry were retrospectively analyzed. FR was defined as 90-day modified Rankin Scale (mRS) scores ≥4 despite successful EVT. Multivariable logistic regression was used to identify independent predictors of FR, and they were used to create a clinical score. The performance of the score was assessed by receiver operating characteristic curve analyses. RESULTS: Of 219 patients, 170 and 49 patients were randomly assigned to the training and validation cohort, respectively. Independent predictors of FR identified in the training cohort were used to construct the SNAP score: site of occlusion (middle cerebral artery = 0, internal carotid artery = 1), National Institutes of Health Stroke Scale score at admission (≤10 = 0, 10 to 19 = 1, ≥20 = 2), age (<75 = 0, ≥75 = 2), and prestroke mRS score (0-3). Receiver operating characteristic curve analyses of the SNAP score in the training and validation cohorts showed areas under the curve of 0.79 (95% CI 0.72-0.86) and 0.79 (95% CI 0.65-0.92) for predicting FR, respectively. A SNAP score ≥5 had a positive predictive value of 92.1% [95% CI 78.8%-97.3%] for FR. CONCLUSION: The SNAP score may be useful in predicting FR after EVT in low-ASPECTS patients with LVO. It can provide patients, family members, and physicians with reliable outcome expectations among patients with acute ischemic stroke with large infarcts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".