Residual Flow After Cerebral Aneurysm Coil Occlusion
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The purpose of this study was to estimate the performance measures of MR angiography (MRA) in the diagnosis of aneurysm residual flow after coil occlusion. METHODS: Patients having at least 1 cerebral aneurysm treated with coil occlusion were prospectively and consecutively enrolled. Time of flight and contrast-enhanced MRA were performed the same day of the DSA follow-up. The degree of aneurysm occlusion and dimensions of the residual flow were evaluated by independent readers at MRA and digital subtraction angiogram. MRA performance measures were estimated in a cross-sectional analysis and repeated in subgroups of aneurysm sizes and locations. MRA predictive values for recurrence were also estimated using a longitudinal design. RESULTS: We obtained 167 aneurysm evaluations for each imaging modality. Class 3 residual flow was seen on digital subtraction angiogram follow-up in 27%. The sensitivity and specificity of MRA was 88% (95% CI, 80-94) and 79% (95% CI, 67-88), respectively. The positive predictive value for a Class 3 recurrence was 67% (95% CI, 51-80) and the negative predictive value was 93% (95% CI, 86-97). Time-of-flight MRA underestimated the length of the residual flow (P=0.039), whereas contrast-enhanced MRA overestimated its width (P<0.0001). MRA sensitivity for a Class 3 residual flow was lower for aneurysms <6 mm (P=0.01). CONCLUSIONS: MRA has sufficient accuracy for screening of aneurysm residual flow after coil occlusion. Due to its lower negative predictive value, recurrent aneurysms should be confirmed with digital subtraction angiogram before planning a retreatment. Routine use of MRA to follow small aneurysms should wait better estimation of its performance in this particular subgroup.
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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 | 0.011 |
| 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 | 0.001 | 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".