Comparison of Computed Tomography Angiography With Digital Subtraction Angiography in the Assessment of Clipped Intracranial Aneurysms
Bibliographic record
Abstract
PURPOSE: To examine whether computed tomography angiography (CTA) is comparable to digital subtraction angiography (DSA) in assessing clipped intracranial aneurysms. MATERIALS AND METHODS: Retrospective collection of clipped aneurysms that had both CTA and DSA within 2 months of one another. Computed tomography angiograms were independently reviewed by 2 blinded neuroradiologists; rereviewed by one at least 4 months later. Each was classified as complete obliteration, neck remnant, or residual aneurysm. Parent vessel was classified as patent or occluded. Digital subtraction angiograms were reviewed in a similar manner by a third blinded neuroradiologist. RESULTS: Forty-eight patients with 53 clipped aneurysms were collected. On DSA, 35 were completely obliterated, 10 neck remnants, and 8 residual aneurysms. The ability of CTA to detect residual aneurysms versus complete obliterations or neck remnants was excellent (mean sensitivity, 88%; specificity, 100%; positive predictive value [PPV], 100%; negative predictive value [NPV], 98%). The ability of CTA to detect neck remnants versus complete obliterations was poor (mean sensitivity, 20%; specificity, 99%; PPV, 83%; NPV, 81%). The CTAs were good at detecting parent vessel occlusion (mean sensitivity, 88%; specificity, 97%; PPV, 75%; NPV, 99%). Interrater and intrarater agreement was good to excellent for aneurysm and parent vessel assessment, with kappa values ranging from 0.6 to 1.0. CONCLUSIONS: Computed tomography angiography has high sensitivity and specificity for residual aneurysm detection and parent vessel occlusion. It is not accurate in neck remnant detection, although these were small and of uncertain clinical significance. This suggests that CTA is useful for follow-up of clipped aneurysms. However, given the potential to miss neck remnants or small residual aneurysms, it is recommended to perform initial DSA and CTA to select cases in which CTA follow-up is appropriate.
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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 | 0.022 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".