Abstract WP54: Evaluation of the e-ASPECTS Automated Software for Detection of Acute Ischemic Stroke
Bibliographic record
Abstract
Objective: The Alberta Stroke Program Early CT score (ASPECTS)method is a validated score for identifying patients more likely to benefit from either thrombolytic or endovascular therapy. The e-ASPECTS software is a new, commercially available, standardized and fully automated ASPECTS scoring tool. e-ASPECTS assesses signs of early ischemic change on plain CT scans of stroke patients by applying the ASPECTS method. We compared the performance of e-ASPECTS to CT perfusion in detecting early ischemic signs. Methods: e-ASPECTS was run on the plain CT scans of 20 patients with acute ischemic stroke. CT perfusion, CT angiography and 24h CT was also available. The ischemic damage identified by e-ASPECTS was compared to the ischemic core as depicted on CBV on CT perfusion and the established infarct core on plain CT at 24h. Findings: e-ASPECTS reliably depicted established ischemic damage as compared to CT-Perfusion, both on an region-based and ASPECTS-based analysis. Conclusion: This is the first report demonstrating that e-ASPECTS correctly identified infarct on plain CT, similar to CT perfusion.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".