Higher Baseline Cortical Score Predicts Good Outcome in Patients With Low Alberta Stroke Program Early Computed Tomography Score Treated with Endovascular Treatment
Bibliographic record
Abstract
BACKGROUND: Patients with large vessel occlusion and noncontrast computed tomography (CT) Alberta Stroke Program Early CT Score (ASPECTS) <6 may benefit from endovascular treatment (EVT). There is uncertainty about who will benefit from it. OBJECTIVE: To explore the predicting factors for good outcome in patients with ASPECTS <6 treated with EVT. METHODS: We retrospectively reviewed 60 patients with ASPECTS <6 treated with EVT in our center between March 2018 and June 2019. Patients were divided into 2 groups because of the modified Rankin Score (mRS) at 90 d: good outcome group (mRS 0-2) and poor outcome group (mRS ≥3). Baseline and procedural characteristics were collected for unilateral variate and multivariate regression analyses to explore the influent variates for good outcome. RESULTS: Good outcome (mRS 0-2) was achieved in 24 (40%) patients after EVT and mortality was 20% for 90 d. Compared with the poor outcome group, higher baseline cortical ASPECTS (c-ASPECTS), lower intracranial hemorrhage, and malignant brain edema after thrombectomy were noted in the good outcome group (all P < .01). Multivariate logistic regression showed that only baseline c-ASPECTS (≥3) was positive factor for good outcome (odds ratio = 4.29; 95% CI, 1.21-15.20; P = .024). The receiver operating characteristics curve indicated a moderate value of c-ASPECTS for predicting good outcome, with the area under receiver operating characteristics curve 0.70 (95% CI, 0.56-0.83; P = .011). CONCLUSION: Higher baseline c-ASPECTS was a predictor for good clinical outcome in patients with ASPECTS <6 treated with EVT, which could be helpful to treatment decision.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".