Abstract T P5: Sequential and Post-procedure ASPECTS Predict Clinical Outcome in Mechanical Thrombectomy of Acute Anterior Circulation Ischemic Stroke
Bibliographic record
Abstract
Purpose: Final ASPECTS has been shown to predict patient outcomes after endovascular therapy in stroke. The goal of this study was to compare sequential ASPECTS imaging pre-treatment and post-treatment in predicting outcome. Methods: The PICS Study is a prospective registry of clinical and imaging data in proximal artery occlusion patients treated with the Penumbra System. In multivariate analysis, variables assessed for relationship to 90 day mRS included age, gender, time to reperfusion, occlusion location, ASPECTS, and NIHSS. ASPECTS scores were assessed by a central core laboratory, blinded except for stroke side. Results: In this study, 141 patients with mean age 67.9 ± 15.6 and median admission NIHSS score 16.0 (IQR 12.0-21.0) met study criteria. Univariate predictors of 90 day mRS included age, baseline NIHSS, 7 day/discharge NIHSS as well as post-treatment ASPECTS. After adjusting for age and baseline NIHSS, post procedure ASPECTS showed a stronger relationship with good outcome (p<0.0001) than pre-treatment ASPECTS (p=0.0520). Change in ASPECTS was also a significant predictor of 90 day mRS (p=0.0046) in the multivariate analysis. Conclusion: Sequential and post procedure ASPECTS are better predictors of clinical outcome following endovascular therapy than pre-ASPECTS. Final infarct volume quantified using ASPECTS serves as a surrogate biomarker for long-term functional outcome.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 0.001 |
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".