SOFIA catheter for direct aspiration of large vessel occlusion stroke: A single-center cohort and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Direct aspiration (DA) using large-bore distal aspiration catheters is an established strategy for the endovascular thrombectomy (EVT) of large-vessel occlusion stroke (LVOS). However, the performance of individual catheters like SOFIA has yet to be examined. METHODS: We present a cohort of 144 consecutive patients treated with first-line DA and SOFIA 6 F Plus catheter for LVOS. We also conducted a systematic review of the literature searching multiple databases for reports on thrombectomy with DA and SOFIA catheters and performed a meta-analysis of recanalization, safety, and clinical outcomes. RESULTS: In the study cohort a successful recanalization (mTICI 2b-3) rate of 75.7% was achieved with DA alone, the global rate for functional independence (90-day mRS 0-2) was 40.3%. For the metanalysis we selected nine articles that included a total of 758 patients treated with first-line thrombectomy with the SOFIA catheters. The mTICI 2b-3 rate was 71.6% (95%CI, 66.3-76.5%) while a rescue stent-retriever was used in 24.1% (95%CI, 17.7-31.9%) of cases. The overall mTICI2b-3 rate after DA and rescue therapy was 88.9% (95%CI, 82.6-93.1%). We found a pooled estimate of 45.6% (95%CI, 38.6-52.8%) for functional independence, a mortality within 90 days of 19% (95%CI, 14.1-25.0%) and a rate of 5.8% (95%CI, 4.2-8.0%) of symptomatic intracranial hemorrhage. CONCLUSION: The DA approach for LVOS with the SOFIA catheters is highly effective with an efficacy and safety profile comparable to those found in contemporary thrombectomy trials and observational studies that use other devices or approaches.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.028 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".