Abstract WP202: Computed Tomography Perfusion-Based Imaging Score Outperform Other Imaging Scores in Basilar Artery Occlusions – An Agreement Study
Bibliographic record
Abstract
Background: Several semi-quantitative imaging scores exist to assess the extent of ischemic injury in basilar artery occlusions using various computed tomography (CT)-based modalities. Their inter-rater agreement has never been compared. Methods: We conducted a retrospective multicenter cohort study of patients with basilar artery occlusions. Four imaging scores (PC-ASPECTS, CAPS, BATMAN, and PC-CTA) were assessed by two raters. Inter-rater agreement was compared using Cohen’s kappa statistic and reliability was assessed using the intraclass correlation coefficient (ICC). Results: 98 patients were included for analysis. The CT perfusion-based CAPS score yielded the highest interrater agreement (kappa 0.64 [95%CI 0.52 – 0.75]) and highest reliability (ICC 0.82 [95%CI 0.73 – 0.91]). By comparison, CTA-based scores achieved fair agreement. The PC-ASPECTS score yielded the lowest levels of agreement overall (kappa 0.11 [95%CI 0.0061 – 0.21]) and in individual regions, with the lowest kappa values for midbrain (kappa 0.098 [95%CI 0.022 – 0.30]). Dichotomization resulted in some improvement in agreement for all approaches. Conclusion: CT Perfusion-based imaging score provide the highest interrater agreement and reliability for assessment of ischemic injury in patients with basilar artery occlusions among common posterior circulation scoring methodologies.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".