Inadvertent Detachment of Stent Retrievers during Mechanical Thrombectomy—A Clinical and Biomechanical Perspective
Bibliographic record
Abstract
BACKGROUND: The inadvertent detachment of stent retrievers during mechanical thrombectomy is an extremely rare but feared complication associated with poor clinical outcomes. We discuss management considerations after an unexpected disconnection of the pRESET stent retriever during mechanical thrombectomy, based on clinical experience and mechanical and phantom studies. METHODS: We present a clinical course of rare accidents of stent-retriever separation inside an intracranial vessel that occurred in patients in a comprehensive stroke centre between 2018 and 2020. We designed a phantom study to assess the Tigertriever's ability to remove a detached stent retriever from intercranial vessels. In the mechanical study, several types of stent retrievers were evaluated in order to find the weakest point at which detachment occurred. RESULTS: Two patients (~0.7%) with inadvertent stent-retriever detachment were found in our database. Failed attempts of endovascular removal with no recanalization at the end of procedure were reported in both cases. mRS after 3 months was three and four respectively. In the mechanical study, the Tigertriever was the most resistant to detachment and was followed by Embotrap > pRESET > 3D Separator. In the phantom study, the pRESET device detached in a configuration resembling the M1 segment was successfully removed with the Tigertriever. CONCLUSIONS: Conservative management of the inadvertent detachment of stent retrievers during mechanical thrombectomy in large vessel occlusion may be acceptable in order to avoid further periprocedural complications after unsuccessful device removal attempts. Based on the phantom and mechanical studies, the Tigertriever may be a useful tool for the removal of detached pRESET devices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".