320-Row Multidetector Computed Tomographic Angiogram in the Evaluation of Cerebral Vasospasm After Aneurysmal Subarachnoid Hemorrhage
Bibliographic record
Abstract
OBJECTIVE: To objectively assess the accuracy of 320-row multidetector computed tomographic (CT) angiography to diagnose cerebral vasospasm after a subarachnoid hemorrhage using a new quantitative method. METHODS: Fifty-four arterial segments were measured in 8 patients who had subarachnoid hemorrhage and underwent digital subtraction angiography within 24 hours after CT angiography for clinical suspicion of cerebral vasospasm. RESULTS: A correlation between arterial diameters measurements made on CT angiography and digital subtraction angiography was observed. The degree of vasospasm tended to be overestimated in the anterior circulation, with arterial diameters that were between 0.05 and 0.72 mm smaller than those on digital subtraction angiography. CONCLUSIONS: A quantitative approach can be used to objectively evaluate the ability of multidetector CT angiography to assess arterial diameter in patients with clinical symptoms of postsubarachnoid hemorrhage cerebral vasospasm. This pilot study also suggests that CT angiography may overestimate the degree of cerebral vasospasm in the anterior circulation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".