Post-Thrombectomy Flat-Panel CT Contrast Staining ASPECTS and Functional Outcome Prediction
Bibliographic record
Abstract
BACKGROUND: As indications for and utilization of mechanical thrombectomy continue to expand, there has been an increasing focus on developing improved tools for functional outcome prediction. We aim to evaluate the reliability of utilizing the areas of contrast staining for the Alberta Stroke Program Early CT Score (s-ASPECTS) rating in immediate post-thrombectomy flat-panel computed tomography and investigate its outcome predictive performance. METHODS: Retrospective analysis of a prospectively collected institutional mechanical thrombectomy database spanning March 2018 to February 2024. s-ASPECTS was calculated. We used ordinal logistic regression models to estimate the relationship between s-ASPECTS and the 90-day modified Rankin Scale and the additional value of these findings to the linear predictor of the Multivariable Outcome Prediction After Endovascular Treatment for Ischemic Stroke (MR-PREDICTS) tool. RESULTS: One thousand sixty-three patients were included in this study with a mean age of 65±15 years, with 53% being male. s-ASPECTS was independently associated with functional outcomes. s-ASPECTS was observed to have more relevance in the clinical predictive model compared with baseline Alberta Stroke Program Early CT Score, as well as to occlusion site and final expanded Thrombolysis in Cerebral Infarction grade. s-ASPECTS was lower among patients with more proximal occlusions, with a lower degree of final reperfusion, with a higher number of passes and longer procedures. The information gathered from flat-panel computed tomography added 18% of new information to MR-PREDICTS, as assessed by the ratio of the variances of the estimated probabilities of good functional outcome with an interobserver consensus score of κ=0.63. CONCLUSIONS: s-ASPECTS was reliably reported and found to be a stronger predictor of outcome compared with baseline Alberta Stroke Program Early CT Score, characterizing it as an important prognostic tool for evaluating functional outcomes of patients following mechanical thrombectomy. Further studies are warranted.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".