Fluoroscopy, CT, and MR imaging characteristics of a novel primarily bioresorbable flow-diverting stent for aneurysms
Bibliographic record
Abstract
BACKGROUND: Five to ten percent of the global population have unruptured intracranial aneurysms, and ruptured brain aneurysms cause approximately 500,000 deaths a year. Flow-diverting stent treatment is a less invasive intracranial aneurysm treatment that induces aneurysm thrombosis. The imaging characteristics of a novel primarily bioresorbable flow-diverting stent (BFDS) are assessed in comparison to the leading metal stent using fluoroscopy, CT, and MRI. METHODS: X-ray/fluoroscopic images of stents were taken using a human cadaveric skull model. CT and MRI were acquired using silicone flow models of residual aneurysms. Images were analyzed with Likert scales in anonymous surveys by neurointerventionalists. Quantitative measurements of radiographic density (CT) and artifact boundary size (CT & MRI) were also obtained. RESULTS: Visibility of the BFDS on X-ray was less than the metal stent but deemed adequate for deployment and intraprocedural assessment. The metal stent was more radiopaque than the BFDS on CT, but qualitative assessment was not significantly different for the two stents. MRI imaging was significantly better using the BFDS in terms of overall artifact and intraluminal assessment. CONCLUSIONS: The BFDS has adequate visualization on X-ray/fluoroscopy and should be clinically acceptable for fluoroscopic deployment. On MRI, there is less quantitative artifact as well as overall improved qualitative assessment that will allow for more detailed non-invasive imaging follow-up of treated aneurysms, potentially reducing the need for digital subtraction catheter angiography.
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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 | 0.002 |
| 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) | 0.002 | 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".