Abstract 116: Conventional and Automated Aspects vs. Ct Perfusion Core Volume to Predict Functional Outcome in Reperfused Acute Ischemic Stroke Patients Undergoing Endovascular Therapy
Bibliographic record
Abstract
Objective: To compare conventional and automated Alberta Stroke Program Early CT score (ASPECTS) to CT perfusion core volumes to predict functional outcome in acute ischemic stroke patients with successful reperfusion after thrombectomy. Materials and methods: Patients from the Computed Tomography Perfusion to Predict Response to Recanalization in ischemic Stroke Project (CRISP) study who achieved mTICI 2b or 3 reperfusion were included. Four independent physicians rated and reached consensus on the conventional ASPECT scores of the baseline CT. Automated ASPECT scores were determined with e-ASPECTS software (Brainomix, Oxford, UK). We used RAPID software (iSchemaView, Stanford, USA) to analyze CT perfusion core volumes. Good and poor functional outcome (GFO and PFO) was defined as a score of 0-2 and 4-6 on the modified Rankin Scale (mRS). Predictors of GFO and PFO were obtained by multivariate logistic regression. Results: We included 156 patients from the CRISP study. Interrater reliability for conventional ASPECTS was excellent (intra-class correlation coefficient 0.77). Median values for conventional ASPECTS, automated ASPECTS, and CT perfusion core volume were 7 (IQR 5-8), 9 (IQR 7-10) and 6.2 ml (IQR 0-17.7). Patients with GFO (59%) had lower baseline NIHSSS, lower rates of diabetes and smaller CTP infarct cores (p < 0.01 for all). Patients with PFO (25%) were older (p < 0.0001), had higher NIHSSS (p = 0.001), higher rates of diabetes (p < 0.0001) and larger CTP infarct cores (p < 0.05). In multivariate analysis CT perfusion core volume was associated with both GFO (OR 0.98; 95%CI 0.96-1.00) and PFO (OR 1.02; 95%CI 1.01-1.04)). Automated ASPECTS was a predictor for GFO (OR 1.26; 95%CI 1.04-1.53) but not PFO. Conventional ASPECTS was not associated with functional outcomes. Conclusion: In the setting of successful thrombectomy, CTP core volume is a better predictor of functional outcome than either conventional or automated ASPECTS.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".