Optimal pinching technique for recanalization: A retrospective analysis of mechanical thrombectomy in a 3D vessel model
Bibliographic record
Abstract
ObjectiveAchieving recanalization with the few passes as possible is essential for favorable outcomes in mechanical thrombectomy (MT). However, when recanalization fails, the underlying reasons often remain unclear because device-thrombus interactions cannot be directly visualized during the procedure. We investigated how device maneuver during the pinching technique influences first pass recanalization (FPR).MethodsUsing a 3D silicone cerebrovascular model and swine thrombi, 109 MT procedures were performed by experienced neurointerventionalists under clinical use fluoroscopy, blinded to direct visualization. Real-world procedural videos were retrospectively analyzed to assess the impact of device maneuvers on FPR.ResultsThe pinching technique for non-segmented thrombus was applied in 58 cases. Advancing the aspiration catheter (AC) to achieve thrombus contact occurred in 52 cases, yielding FPR in 44 cases. Pulling the stent retriever (SR) toward the AC after contact was performed in 14 cases, all achieving FPR, compared with 32 of 44 cases without this maneuver. SR deployment after AC-thrombus contact frequently caused proximal AC displacement and loss of thrombus contact.ConclusionIn pinching technique, advancing the AC to ensure thrombus contact, followed by pulling the SR toward the AC before system retrieval, may prevent stretching, facilitate effective pinching, and improve the likelihood of achieving FPR.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".