Automated versus Manual Imaging Assessment of Early Ischemic Changes in Acute Stroke -Comparison of two Software Packages and Expert Consensus
Bibliographic record
Abstract
Aim: The purpose of our study was to compare the accuracy of both the total Alberta Stroke Program Early CT Score (ASPECTS) and region-based scores from two automated ASPECTS software packages and an expert consensus reading (EC) in patients who had prompt reperfusion from endovascular thrombectomy (EVT). Methods: ASPECTS were retrospectively and blindly assessed by two software packages and EC on baseline non-contrast enhanced computed tomography (NCCT) images. All patients had multimodal CT imaging including NCCT, CT-angiography and CT-perfusion which demonstrated an acute anterior circulation ischemic stroke with a large vessel occlusion. Final ASPECTS on follow-up scans in patients who had EVT and achieved complete reperfusion within 100 min from NCCT served as ground truth and were compared to total and region-based scores. Results: Fifty-two patients met our study criteria. Good agreement was obtained between the software packages and EC for total ASPECTS but the two software packages differed significantly with respect to regional contribution. EC and one software package achieved a better agreement for region-based scoring and both were superior to the other software. One software more commonly identified cortical areas as abnormal and less often identified deep structures, while the other software more frequently identified deep structures as abnormal and less commonly identified cortical areas; P < 0.0001. Conclusion: Using the follow-up ASPECTS as ground truth, significant differences in accuracy were documented between the software programs.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".