F.05 Characterization of NBCA glue polymerization for embolization of brain AVM’s
Bibliographic record
Abstract
Background: Brain arteriovenous malformations (AVM’s) are abnormal connections between arteries and veins. Endovascular glue embolization with N-butyl cyanoacrylate (NBCA) is an accepted form of treatment, with most complications related to timing of polymerization. Current literature reports a wide range of polymerization times with large discrepancies between in-vivo and in-vitro results. Methods: Polymerization time was measured for mixtures of lipiodol/NBCA of 50/50, 60/40, 70/30. The influence of pH, temperature and presence of biological catalysts on polymerization rate was investigated in-vivo using submerged droplet tests. PVA-C, silicone and endothelium surfaces were compared and contact angles were measured to assess physical interaction with NBCA. High-speed video of glue injection through a microcatheter was captured to characterize coaxial flow. Results: Polymerization rate increases with pH and temperature. A hydrophilic substrate such as PVA-C provides surface properties that are most similar to endothelium. Endothelium provides a catalytic surface that increases the rate of polymerization. Blood products further increase the polymerization rate with RBC’s providing almost instantaneous polymerization of NBCA upon contact. Characterization of coaxial flow shows dripping to jetting transition with significant wall effect. Conclusions: We have successfully deconstructed and characterized the dynamic behavior of NBCA embolization. A refined understanding of NBCA behavior could help reduce embolization-related complications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".