Abstract TMP63: Predictors And Clinical Impact Of ASPECTS Evolution After Successful Reperfusion With Endovascular Therapy: Insight From The ETIS Registry
Bibliographic record
Abstract
Background: Alberta Stroke Program Early CT scan Score (ASPECTS) is a reliable imaging biomarker of infarct extension in patients with large vessel occlusions. ASPECTS evolution, a surrogate of infarct expansion, is an important predictor of functional and safety outcomes after endovascular therapy (EVT). In this study, we aimed to identify the predictors of ASPECTS evolution after successful reperfusion and the association between ASPECTS evolution and outcomes of EVT. Methods: We used data from the ongoing prospective multicenter Endovascular Treatment in Ischemic Stroke (ETIS) registry (NCT03776877). For the purpose of this study, we enrolled patients with anterior circulation LVO treated with EVT and achieved successful reperfusion (mTICI 2b-3). Additional inclusion criteria included 1) the availability of ASPECTS score on admission and at 24 hours after EVT 2) ASPECTS was assessed on the same imaging technique (i.e MRI or CT) on admission and at 24 hours. We considered 2 or more points decrease in ASPECTS as a significant ASPECTS change. Multivariable logistic regression analyses were used to identify the predictors of ASPECT evolution and to study the association between ASPECTS evolution and outcomes. Results: We included a total of 1161 patients, of whom 978 (84%) patients had at least 2 points decrease in ASPECTS score. Worsening ASPECTS score was associated with higher odds of poor functional outcome (90-day mRS 3-6), mortality, and symptomatic intracerebral hemorrhage. Admission ASPECTS, NIHSS, blood glucose, location of occlusion, final mTICI score, total number of attempts and procedure time emerged as predictors of ASPECTS evolution. Conclusion: ASPECTS evolution is a strong predictor of clinical and safety outcomes after successful reperfusion with EVT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".