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 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) | 0.000 | 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".