Development of a new generation of neurovascular device for the treatment of cerebral bifurcation aneurysms
Bibliographic record
Abstract
Cerebral (brain) aneurysm (CA), is an abnormal dilation of the cerebral arterial wall, which accounts for more than half a million deaths each year worldwide. It is well recognized that hemodynamic factors play an important role in aneurysm development and propagation. Flow diverters (FDs) represent one method recently developed in treating CAs . Evasc Medical System Inc., whose area of expertise is developing novel CA therapies, has recently introduced a novel FD (eCLIPs) for the percutaneous treatment of bifurcation cerebral aneurysms. Unlike other devices for such treatment, eCLIPs is implanted at the daughter arteries and does not impede the flow in the main or daughter arteries. Prior studies in the literature clearly indicate the advantages of eCLIPs over conventional FDs, such as Pipeline (Medtronic product), in disrupting the flow at the aneurysm neck. In spite of its advantages over other FDs in the market, there are some shortcomings pertaining to eCLIPs design, including blood flow leakage through the gap between the device and parent artery where there is a fusiform-like pathology. In partnership with Evasc, we have developed a new design for eCLIPs for particular aneurysms that have fusiform-like properties involving the confluence of the main and daughter branches.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".