Ballast and NeuronMax in stroke thrombectomy
Bibliographic record
Abstract
BACKGROUND: Comparative evaluation of long sheath performance in stroke thrombectomy has not been performed. OBJECTIVE: To review an initial experience with the new Ballast 6F long sheath compared with the NeuronMax, to evaluate comparative benchmarks in trackability, navigability, and procedural outcomes. METHODS: A prospectively maintained thrombectomy database was evaluated over a 6-month period to compare procedural and angiographic results between a cohort of patients treated with the historical institutional standard long sheath (NeuronMax) and another with the new Ballast long sheath via a transfemoral approach. RESULTS: Of 156 stroke thrombectomy cases, 69 were performed using NeuronMax and 40 using Ballast via a transfemoral approach; the remainder of cases employed alternative long sheaths or were performed via initial radial access. There was no significant difference in patient age, medical history, baseline National Institutes of Health Stroke Scale score, Alberta Stroke Program Early CT Score, arch type, tissue plasminogen activator use, and clot location between the two groups. Single-pass case frequency (41% for NeuronMax vs 44% for Ballast, p=0.84), and final successful revascularization (TICI 2b or greater) were similar between the two cohorts (91% vs 98%, p=0.42). Good 90-day outcome (modified Rankin Scale score 0-2) was also similar (33% for NeuronMax, 43% for Ballast, p=0.41). Excluding tandem occlusions, mean procedural time was 31 min for NeuronMax and 25 min for Ballast (p=0.09). Puncture to long sheath access and angiography in the base target vessel was faster for Ballast than NeuronMax (6.5 min vs 9.2 min, p=0.04). CONCLUSION: Among a cohort of practitioners with historical, preferential experience with NeuronMax for stroke thrombectomy, faster procedural times were achieved with Ballast with similar final angiographic results.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".