Image Based Automated ASPECT Score for Acute Ischemic Stroke Patients
Bibliographic record
Abstract
The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a tool to assess early ischemic changes in acute stroke patients and found to be superior when applied to contrast-based image modalities. We hypothesized that automated ASPECTS scores have no differences with the manual scoring. We generated time-invariant CTA (tiCTA) from CT Perfusion dataset and measured the ASPECTS score automatically and manually. Statistical analysis was performed to see the differences. The association of both measurements with patient outcome was measured using NIH stroke scale at admission, final infarct size, and 3-month modified Rank Scale (mRS) determined using the Spearman correlation coefficient. As a result, the difference between automated and manual ASPECT scores was statistically not significant (p=0.18). Both automated ASPECTS scores were identical in 40% of the patients for the total score. While all ASPECTS scores have limited association with outcome, our study illustrates the usability of automated ASPECTS applied on tiCTA, allowing simplification of CT workflow for acute ischemic stroke patients and promoting faster analysis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".