Region-specific agreement in ASPECTS estimation between neuroradiologists and e-ASPECTS software
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The Alberta Stroke Program Early CT Score (ASPECTS) is a widely used measure of ischemic change on non-contrast CT. Although predictive of long-term outcome, ASPECTS is limited by its modest interobserver agreement. One potential solution to this is the use of machine learning strategies, such as e-ASPECTS, to detect ischemia. Here, we compared e-ASPECTS with manual scoring by experienced neuroradiologists for all 10 individual ASPECTS regions. MATERIALS AND METHODS: We retrospectively reviewed 178 baseline non-contrast CT scans from patients with acute ischemic stroke undergoing endovascular thrombectomy. All scans were reviewed by two independent neuroradiologists with a third reader arbitrating disagreements for a consensus read. Each ASPECTS region was scored individually. All scans were then evaluated using a machine learning-based software package (e-ASPECTS, Brainomix). Interobserver agreement between readers and the software for each region was calculated with a kappa statistic. RESULTS: The median ASPECTS was 9 for manual scoring and 8.5 for e-ASPECTS, with an overall agreement of κ=0.248. Regional agreement varied from κ=0.094 (M1) to κ=0.555 (lentiform), with better performance in subcortical regions. When corrected for the low number of infarcts in any given region, prevalence-adjusted bias-adjusted kappa ranged from 0.483 (insula) to 0.888 (M3), with greater agreement for cortical areas. Intraclass correlation coefficients were between 0.09 (M1) and 0.556 (lentiform). CONCLUSION: Manual scoring and e-ASPECTS had fair agreement in our dataset on a per-region basis. This warrants further investigation using follow-up scans or MRI as the gold standard measure of true ASPECTS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".