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 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 | 0.001 |
| 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) | 0.001 | 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".