CT angiography for the detection of cerebral vasospasm in patients with acute subarachnoid hemorrhage.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Digital subtraction angiography (DSA) is the standard of reference for detecting cerebral vasospasm after subarachnoid hemorrhage (SAH). CT angiography (CTA) is a relatively recent method for depicting the intracranial arterial vasculature. The purpose of this study was to compare CTA and DSA in the detection and quantification of cerebral vasospasm. METHODS: Seventeen patients with SAH underwent initial CTA with or without DSA and follow-up CTA and DSA. The follow-up CTA and DSA studies were performed within 24 hours of each other and 5 to 10 days after SAH. Maximum intensity projection images were produced for each CTA. Six arterial locations were examined for spasm: the suprasellar internal carotid artery (ICA), the M1 and M2 segments of the middle cerebral artery, the A1 and A2 segments of the anterior cerebral artery, and the basilar artery. Vasospasm was categorized as none, mild (<30% luminal reduction), moderate (30% to 50% reduction), or severe (>50% reduction). RESULTS: The overall correlation between CTA and DSA was 0.757, but was better for proximal than distal locations (0.88-1.00 versus 0.152-0.446). Agreement between CTA and DSA was greater for no spasm (92%) and severe spasm (100%) than for mild (57%) or moderate (64%) spasm. CTA was highly accurate for no spasm or severe spasm in proximal locations (96%, and 100%, respectively); it was less accurate (90% and 95%, respectively) for mild or moderate spasm in these locations. For distal locations, the accuracy for absent, mild, moderate, or severe spasm was 78%, 81%, 94%, and 100%, respectively. CONCLUSION: CTA is highly sensitive, specific, and accurate in detecting no spasm or severe cerebral vasospasm in proximal arterial locations; it is less accurate for detecting mild and moderate spasm in distal locations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".