P.113 Validation and standardization of cerebral vasospasm grading on CT angiography
Bibliographic record
Abstract
Background: The diagnosis of cerebral vasospasm, either by digital subtraction angiography (DSA), or now more commonly by computerized tomographic angiography (CTA) occurs in up to 70% of patients with aneurysmal subarachnoid hemorrhage (aSAH). The lack of standardization among vasospasm grading has made its clinical correlation with delayed cerebral ischemia challenging Methods: 36 of the 764 aSAH patients found on the St. Michael’s Hospital RIS database had both DSA and CTA performed, at time of admission and again between day 2 and 14 following SAH. Two blinded neuroradiologists graded all vessels for vasospasm on two separate scales, by consensus for DSA and independently for CTA Results: Comparing CTA and DSA, Grading Scale (GS)1 had the highest Spearman Correlation Coefficient (SCC): 0.691 (P<0.001) for Rater (R)1, and 0.687 (P<0.001) for R2. SCC was higher when only considering proximal vessels. Cohen’s Kappa (CK) measuring inter-rater reliability was 0.695 (P<0.001) for GS2 and 0.681 (P<0.001) for GS1. CK was higher in anterior circulation vessels, and tended to decrease with increasing vasospasm grade. Conclusions: Although either scale will provide the benefits of standardization to clinical practice and research, GS1 is recommended as it is more intuitive and provides higher SCCs, with only slightly lower CKs.
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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 | 0.050 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".