Re-treatment of residual aneurysms after flow diversion: An experimental study
Bibliographic record
Abstract
Aim Flow diverters are increasingly used to treat aneurysms, but treatment is not always effective. The management of aneurysms that fail to occlude following flow diversion is problematic. We aimed to reproduce failures in an animal model and study re-treatment with additional flow diverters alone or with flow diverters and liquid embolic agent. Material and methods Twenty wide-necked aneurysms were created at the carotid-lingual bifurcation in 10 dogs, and were treated with flow diverters 4-6 weeks later. Follow-up angiography was performed at three months. Suitable residual aneurysms were randomly allocated: re-treatment with flow diverters alone ( n = 6), or with the injection of liquid embolic between two layers of flow diverters ( n = 4) or no re-treatment ( n = 2). Angiography was repeated three months later, followed by euthanasia, photography and pathology. Results Patent wide-necked aneurysms were produced in 17/20 attempts (85%); three months after flow diversion there were 15/17 (88%) residual aneurysms. In three cases, re-treatment was not possible because the flow diverter had prolapsed into the aneurysm, leaving 12 aneurysms to study. Re-treated aneurysms showed improved angiographic results at six months (median score of 2; P = 0.03), but residual aneurysms were present in all cases. Parent artery occlusion occurred in two aneurysms treated with flow diverter plus liquid embolic. At pathology, aneurysms were only partially filled with thrombus; leaks through the flow diverters were found in the neointima connecting the arterial lumen to residual aneurysms. Conclusion Re-treatment of residual flow-diverted experimental aneurysms with additional flow diverters did not lead to 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".