Impact of balloon guide catheter on technical and clinical outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Flow arrest with balloon guide catheters (BGCs) is becoming increasingly recognized as critical to optimizing patient outcomes for mechanical thrombectomy. We performed a systematic review and meta-analysis of the literature for studies that compared angiographic and clinical outcomes for patients who underwent mechanical thrombectomy with and without BGCs. MATERIALS AND METHODS: In April 2017 a literature search on BGC and mechanical thrombectomy for stroke was performed. All studies included patients treated with and without BGCs using modern techniques (ie, stent retrievers). Using random effects meta-analysis, we evaluated the following outcomes: first-pass recanalization, Thrombolysis In Cerebral Infarction (TICI) 3 recanalization, TICI 2b/3 recanalization, favorable outcome (modified Rankin Scale (mRS) 0-2), mortality, and mean number of passes and procedure time. RESULTS: Five non-randomized studies of 2022 patients were included (1083 BGC group and 939 non-BGC group). Compared with the non-BGC group, patients treated with BGCs had higher odds of first-pass recanalization (OR 2.05, 95% CI 1.65 to 2.55), TICI 3 (OR 2.13, 95% CI 1.43 to 3.17), TICI 2b/3 (OR 1.54, 95% CI 1.21 to 1.97), and mRS 0-2 (OR 1.84, 95% CI 1.52 to 2.22). BGC-treated patients also had lower odds of mortality (OR 0.52, 95% CI 0.37 to 0.73) compared with non-BGC patients. The mean number of passes was significantly lower for BGC-treated patients (weighted mean difference -0.34, 95% CI-0.47 to -0.22). Mean procedure time was also significantly shorter for BGC-treated patients (weighted mean difference -7.7 min, 95% CI-9.0to -6.4). CONCLUSIONS: Non-randomized studies suggest that BGC use during mechanical thrombectomy for acute ischemic stroke is associated with superior clinical and angiographic outcomes. Further randomized trials are needed to confirm the results of this study.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.034 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".