ASPECTS decay during inter-facility transfer predicts patient outcomes in endovascular reperfusion for ischemic stroke: a unique assessment of dynamic physiologic change over time
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Bibliographic record
Abstract
BACKGROUND: Pretreatment Alberta Stroke Program Early CT Scores (ASPECTS) is associated with clinical outcomes. The rate of decline between subsequent images, however, may be more predictive of outcomes as it integrates time and physiology. METHODS: A cohort of patients transferred from six primary stroke centers and treated with intra-arterial therapy (IAT) was retrospectively studied. Absolute ASPECTS decay was defined as ((ASPECTS First CT-ASPECTS Second CT)/hours elapsed between images). A logistic regression model was performed to determine if the rate of ASPECTS decay predicted good outcomes at 90 days (modified Rankin Scale score of 0-2). RESULTS: 106 patients with a mean age of 66±14 years and a median National Institutes of Health Stroke Scale score of 19 (IQR 15-23) were analyzed. Median time between initial CT at the outside hospital to repeat CT at our facility was 2.7 h (IQR 2.0-3.6). Patients with good outcomes had lower rates of absolute ASPECTS decay compared with those who did not (0.14±0.23 score/h vs 0.49±0.39 score/h; p<0.001). In multivariable modeling, the absolute rate of ASPECTS decay (OR 0.043; 95% CI 0.004 to 0.471; p=0.01) was a stronger predictor of good patient outcome than static pretreatment ASPECTS obtained before IAT (OR 0.64; 95% CI 0.38 to 1.04; p=0.075). In practical terms, every 1 unit increase in ASPECTS decline per hour correlates with a 23-fold lower probability of a good outcome. CONCLUSIONS: Patients with faster rates of ASPECTS decay during inter-facility transfers are associated with worse clinical outcomes. This value may reflect the rate of physiological infarct expansion and thus serve as a tool in patient selection for IAT.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 it