Postoperative Assessment of Clipped Aneurysms With 64-Slice Computerized Tomography Angiography
Bibliographic record
Abstract
BACKGROUND: Multidetector computerized tomography angiography (MDCTA) is now a widely accepted technique for the management of intracranial aneurysms. OBJECTIVE: To evaluate its accuracy for the postoperative assessment of clipped intracranial aneurysms. METHODS: We analyzed a consecutive series of 31 patients that underwent direct surgical clipping procedures of 38 aneurysms. A 64 slice MDCT scanner (Aquilion 64, Toshiba) was used and results were compared with digital subtraction angiographies (DSA). Two independent neuroradiologists analyzed the following data: examination quality, artifacts, aneurysm remnant, and patency of collateral branches. Interobserver agreement, sensitivity, and specificity were calculated. RESULTS: Seventy-nine percent of the aneurysms were located in the anterior circulation. Significant artifacts were found with multiple and cobalt-alloy clips. According to DSA, remnants >2 mm were found in 21% of the cases, and 2 patients had one collateral branch occluded. Sensitivity and specificity of 64-MDCTA for the detection of aneurysm remnants were 50% and 100%, respectively. Sensitivity and specificity of 64-MDCTA for the detection of a significant remnant (>2 mm) and the detection of the occlusion of a collateral branch were, respectively, 67% and 100% and 50% and 100%. No relationship was found with the location, type, shape, size, or number of clips, but missed remnants tended to be larger with cobalt-alloy clips. CONCLUSIONS: 64-MDCTA is a valuable technique to assess the presence of a significant postoperative remnant in single titanium clip application cases and might be useful for long-term follow-up. DSA remains the most accurate postoperative radiological examination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".