Immediate flow-diversion characteristics of a novel primarily bioresorbable flow-diverting stent
Bibliographic record
Abstract
OBJECTIVE: Flow-diverting stents with a resorbable component have significant theoretical benefits over full metal stents, although currently there are none in clinical use. In this study, the authors sought to determine the immediate flow-diversion characteristics of a novel primarily bioresorbable flow-diverting stent. METHODS: Bioresorbable stents were deployed into glass tube models to determine porosity and pore density. In vitro flow diversion behavior was evaluated using high frame rate angiography under pulsatile flow conditions in a patient-specific silicone aneurysm model treated with the resorbable stent as well as the Surpass Evolve stent. In vivo flow diversion was characterized by deployment into 20 rabbit saccular aneurysm models, and grading was based on the O'Kelly-Marotta scale and the 4F-flow diversion predictive score. RESULTS: Porosities and pore densities of the bioresorbable stent were in the flow-diverting range for all target vessel diameters. Quantified results of immediate angiography after placement of the bioresorbable stent into a silicone aneurysm model demonstrated greater flow diversion compared to the Evolve stent. Bioresorbable stent placement in saccular aneurysm models resulted in an immediate O'Kelly-Marotta grade of A3 or better and a 4F-flow diversion predictive score of 4 or better in all cases. CONCLUSIONS: The bioresorbable stent has immediate flow-diversion characteristics that are comparable to commercially available metal stents. Longer-term studies are underway to determine the ability of the resorbable fibers to act as a neointimal scaffold and result in long-term aneurysm occlusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".