O25 eCLIPs implant in the endovascular treatment of wide neck aneurysms (WNBA)
Bibliographic record
Abstract
Introduction eCLIPs combines both coil retention and flow diversion by bridging the neck of WNBA. We describe eCLIPs implant experience to date using 2 delivery systems: i) via hypotube with requisite .034’ microcatheter, eBRS, and ii) via guidewire, compatible with smaller microcatheters, eB. Aim of Study We report the entire eCLIPs experience for safety and efficacy: 280 patients, 101 eBRS and 179 eB. Methods Procedural data were collected prospectively and follow up data were collected according to usual clinical practices. Results Implant procedural success rate was 88%, 82% for eBRS, 92% for eB. 35% of cases were recurrences, 20% prior rupture. Anatomy: 89% cases were basilar tip and carotid terminus locations. Mean followup 38 mo: Safety: Procedural neurologic death: 3% eBRS, 0% eB (overall 1.1%), CVA 3% eBRS, 1.1% eB (overall 1.8%), repeat procedure (all with inadequate neck-bridging) 2.5%. No patient had post-procedural thrombo-embolic or other neurologic safety events. Efficacy: Of 82 patients with eBRS implants, 61 meet eligibility criteria for a WEB-IT-patterned trial; all available 57 patients of this cohort had follow-up imaging, many with multiple imaging timepoints. Forty patients with eB implants have had follow-up > 6 months. Modified Raymond Roy Occlusion Classification (for all patients) 1 = 80%, 2 = 16%. No patient had regression of mRROC score on serial follow-up imaging. Conclusion eCLIPs has a satisfactory safety profile; introduction of lower-profile eCLIPs delivery system, eliminating large microcatheters and complex sidebranch access, resulted in lower procedural complications and improved procedural success. Durable adequate occlusion is 96%. Disclosure of Interest yes Co-founder Evasc Neurovascular.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".