Stent Retriever Thrombectomy with Mindframe Capture LP in Isolated M2 Occlusions.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE Mechanical thrombectomy is an effective recanalization technique in acute ischemic stroke patients with large vessel occlusions; however, it is unclear to what extent stent retriever thrombectomy may be applicable to occlusions of smaller peripheral cerebral vessels. The outcome of patients with isolated M2 occlusions treated with the Mindframe Capture low profile (LP) stent retriever was reviewed. MATERIAL AND METHODS A retrospective review of prospectively collected data on all consecutive patients treated for isolated M2 occlusions between June 2013 and December 2017 using the Mindframe Capture LP was performed. Technical aspects of the recanalization procedure, recanalization rate, complication rate, and clinical outcome were analyzed. RESULTS Mechanical thrombectomy with the Mindframe Capture LP was performed in 38 patients (median age 79 years) with an isolated M2 occlusion. The median National Institutes of Health Stroke Scale (NIHSS) score on admission was 7.5 (interquartile range, IQR 5-12) and successful reperfusion modified Thrombolysis in Cerebral Infarction (mTICI 2b or 3) was achieved in 28 patients (74%). A compensated/adjusted modified Rankin Scale (mRS) 0-2 at 3 months was observed in 65% when taking pre-stroke disability into account. Symptomatic intracranial hemorrhage (sICH) occurred in 1 patient (2.6%). Asymptomatic intracranial hemorrhage (aICH) was noted in 8 patients (21%) and a small subarachnoid hemorrhage (SAH) in the immediate vicinity of the target vessel was apparent in 8 patients (21%). CONCLUSION The Mindframe Capture LP is a technically effective thrombectomy device for the treatment of isolated M2 occlusions. The lower profile of the device is advantageous when targeting peripheral intracranial occlusions.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".