Bench-to-bedside: Use of particle image velocimetry to assess iterative development of cerebral bifurcation flow diverter and its proof of principle
Bibliographic record
Abstract
Abstract Brain aneurysms are at risk of rupture causing death or disability. Many treatments exist for wide neck bifurcation aneurysms, the most common type of aneurysm, but none have become definitive standard of care because a variety of deficiencies exist for each. Flow diversion (FD) with tubular FDs has emerged as definitive treatment for sidewall aneurysms, causing sufficient reduction in aneurysm flow velocity that blood stasis and thrombosis occurs, leading to complete occlusion of the aneurysm. Tubular FD at bifurcations, however, are inadequate, leading to incomplete neck coverage and obstruction of blood flow to side branches. This paper describes the use of particle imaging velocimetry to evaluate iterative design changes to achieve a true bifurcation FD, capable of sufficiently reducing aneurysm flow velocity to produce prolonged stasis and ultimately leading to complete aneurysm occlusion without the use of adjuvant intrasaccular contents such as coils.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".