Flow-diverter stents in the endovascular treatment of remnants in previously clipped ruptured aneurysms: a feasibility study
Bibliographic record
Abstract
BACKGROUND: The rate of intracranial aneurysm remnants/recurrences after microsurgical clipping varies widely. The optimal management for these patients remains a matter of debate. Repeat surgery in particular bears a high risk of periprocedural complications due to anatomical distortion from prior procedures. This study aims to evaluate the risk-benefit profile of flow-diverter stents in these patients. METHODS: The patient database of our neurovascular centre was queried to identify patients with clipped aneurysms who subsequently underwent endovascular treatment with intraluminal flow-diverter stents. The outcome analysis consisted of an assessment of clinical parameters (modified Rankin scale) and the post-interventional angiographic occlusion status (according to the Raymond-Roy occlusion classification). RESULTS: Six patients underwent endovascular treatment with flow-diverter stents of recurrent aneurysms after clipping. Treatment was necessary in two patients due to progressive neurological deficits, and due to angiographic proof of an increasing aneurysm size in the other four patients. Median aneurysm size was 0.45 cm. All patients had a prior history of subarachnoid haemorrhage. The time from primary clipping to recurrence was 10.6 years. Complete radiological aneurysm occlusion was feasible in five out of six cases. Two patients who had experienced pre-interventional neurological deficits showed a complete remission of symptoms on last follow-up. No periprocedural morbidity or mortality was recorded and no patient required retreatment within the median follow-up. CONCLUSION: This case series suggests that endovascular treatment with flow-diverter stents of aneurysm remnants after previous microsurgical clipping is a feasible treatment concept with a low-risk profile, which might prevent the treatment burden and risks of repeat surgery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".