A cortically-weighted versus total Alberta Stroke Program Early Computed Tomography Score in thrombectomy outcome models
Bibliographic record
Abstract
OBJECTIVES: Individual subcortical infarct scoring for the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) can be difficult and is subjected to higher inter-reader variability. This study compares performance of the 10-point ASPECTS with a new 7-point cortically-weighted score in predicting post-thrombectomy functional outcomes. MATERIALS AND METHODS: Prospective registry data from two comprehensive stroke centers (Site 1 2016-2021; Site 2: 2019-2021) included patients with either M1 segment of middle cerebral artery or internal carotid artery occlusions who underwent thrombectomy. Two multivariate proportional odds training models utilizing either 10-point or 7-point ASPECTS predicting 90-day shift in modified Rankin score were generated using Site 1 data and validated with Site 2 data. Models were compared using multiclass receiver operator characteristics, corrected Akaike's Information Criterion, and likelihood ratio test. RESULTS: Of 328 patients (Site 1 = 181, Site 2 = 147), median age was 71y (IQR 61-82), 119 (36%) had internal carotid artery occlusions, and median 10-point ASPECTS was 9 (IQR 8-10). There was no difference in performance between models using either total or cortically-weighted ASPECTS (p=0.14). Validation cohort data were correctly (i.e., predicting modified Rankin score within one point) classified 50% (cortically-weighted score model) and 56% (total score model) of the time. CONCLUSIONS: The 7-point cortically-weighted ASPECTS was similarly predictive of post-thrombectomy functional outcome as 10-point ASPECTS. Given noninferior performance, the cortically-weighted score is a potentially reliable, but simplified, alternative to the traditional scoring paradigm, with potential implications in automated image analysis tool development.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".