A Weighted Cortical ASPECTS Compared Against Total ASPECTS in Outcome Prediction of Anterior Large Vessel Occlusion Thrombectomy (S24.007)
Bibliographic record
Abstract
Objective: Compare outcome prediction using Cortical Alberta Stroke Program Early Computed Tomography Score(ASPECTS) against total ASPECTS, in large vessel occlusion strokes undergoing Thrombectomy. Background: The 10-point ASPECTS accounts for subcortical structures that may be more likely to infarct and differentially associated with long-term outcomes when compared to injured cortical regions. More specific, cortically-weighted 7-point ASPECTS score could more accurately predict long-term functional outcomes following successful recanalization. Design/Methods: Registry data from 2 Comprehensive Stroke Centers(site 1:2016–2021; site 2:2019–2021) including patients with M1 or internal carotid artery(ICA) occlusions who underwent thrombectomy were consolidated for analysis. The primary outcome was a shift in 90-day modified Rankin(mRS), which was adjusted a priori for age, pre-stroke mRS, baseline National Institutes of Health Stroke Scale, M1 versus internal carotid artery (ICA) occlusion, and successful recanalization(TICI 2b/3). The training model of 10-point ASPECTS(tASPECTS) vs. 7-point cortical-weighted ASPECTS(cASPECTS) was generated in site 1 and validated in site 2, with models compared using receiver operator characteristics(ROC) and corrected Akaike’s Information Criterion(AICc). Results: Of 328 included patients (site 1/training cohort=181; site 2/validation=147), median age was 71y(IQR 61.25–82), of whom 119(36.28%) had ICA occlusion, with a median tASPECTS of 9(IQR 8–10) and median cASPECTS of 6(IQR 5–7). In derivation cohort for the cASPECTS, multi-class adjusted ROC curves were generated, with areas under the curve (AUC) for predicting mRS shift ranging from 0.8546–0.9110(AICc=690.078, r^2=0.5936). In validation cohort, the misclassification rate was 71.6%, however, when expanded for flexibility(+/−1 mRS unit), model classifies validation site data correctly 55.8% of time. In training cohort for the tASPECTS, the AUC for predicted mRS classes ranged from 0.8812–0.9250(AICc=693.646, r^2=0.6165), with no difference in model performance against cASPECTS (p=0.14). In validation cohort, the misclassification rate was 73.47%, and improved to 50.3% correct when expanded for flexibility(+/−1 mRS unit). Conclusions: The cASPECTS and tASPECTS were similarly predictive of 90-day mRS scores following successful recanalization. Disclosure: Dr. Patel has nothing to disclose. Ms. Koneru has nothing to disclose. Ms. Penckofer has nothing to disclose. Dr. Nguyen has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Vesalio. Dr. Nguyen has received personal compensation in the range of $0-$499 for serving on a Scientific Advisory or Data Safety Monitoring board for NIH. Dr. Nguyen has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Avania. Dr. Nguyen has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for AHA. The institution of Dr. Nguyen has received research support from Boston Medical Center. The institution of Dr. Nguyen has received research support from Society of Vascular and Interventional Neurology. Dr. Khalife has nothing to disclose. Dr. Oliveira has nothing to disclose. Mohamad Abdalkader has nothing to disclose. Mr. Klein has nothing to disclose. Mr. Vigilante has nothing to disclose. Scott Kamen has nothing to disclose. Dr. Thomas has received personal compensation for serving as an employee of Cooper Univeristy Health Care. Dr. Thomas has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Stryker. Dr. Thomas has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Medtronic. Dr. Thomas has received personal compensation in the range of $500-$4,999 for serving as a Consultant for CereVasc. Dr. Siegler has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Ceribell. Dr. Siegler has received personal compensation in the range of $10,000-$49,999 for serving on a Speakers Bureau for AstraZeneca.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".