P28 Treatment of complex wide-neck aneurysms using WEB device – a single center 8 years experience
Bibliographic record
Abstract
Introduction Treating intracranial complex wide-neck aneurysms with endovascular therapy is difficult. The WEB device is designed to treat aneurysms that are wide-necked and complex. Aim of study We present 8-year experience with the WEB device for the treatment of brain aneurysms, as well as second-stage treatment techniques in selected cases. Scientific papers have confirmed the safety and efficacy of aneurysm embolization with the WEB device. Methods Clinical information, as well as DSA images, were gathered and analysed. The RROC Scale and the Modified Montreal Scale were used to assess aneurysm occlusion. Results A total of 156 patients were included in the study, with 162 aneurysms treated with the WEB device. The MCA bifurcation was the most common site. The majority of aneurysms were not ruptured. With the exception of six patients with unruptured aneurysms, all patients had a good clinical outcome, and no new neurological deficits associated with the device. The vast majority of aneurysms had adequate occlusion. A stent was used in some non-ruptured cases to prevent device protrusion. The second stage of treatment consisted of stent implantation and coiling of the aneurysm in cases where the aneurysm recanalized. Conclusions The WEB device is safe and efficient, and it provides good occlusion in ruptured and unruptured aneurysms, according to experience and results. Recanalization and device compression are caused by incorrect device sizing and may necessitate reembolization. Despite the need for second-stage treatment in some cases, most aneurysms could be managed without dual antiplatelet therapy, which is essential for patients. References Material from own research. Do you have any conflict of interest to declare?: No
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".