Flow diversion to treat aneurysms: the free segment of stent
Bibliographic record
Abstract
PURPOSE: Flow diverters (FDs) have led to spectacular results in otherwise untreatable aneurysm cases, but complications can occur. There is a pressing need to study factors that might predict their safety and efficacy. METHODS: The anatomical constraints that may impact on the ability of FDs to redirect blood flow and provide a scaffold for neointima formation across the aneurysm or branch ostia are explored and classified. A nomenclature is needed to identify the key factors that should be taken into account before contemplating the use of FDs in clinical aneurysms, and that should be reproduced in experimental models, if they are to guide safe clinical use. RESULTS: The free stent segment (FSS), the portion of the device that covers an aneurysm or branch origin, dictates whether aneurysms or branches will remain patent. Three levels of increasing complexity must be taken into account to anticipate what will occur at the FSS level. (1) Virtual models can provide basic principles; (2) in vitro studies allow testing FSS deformations that may occur in various anatomical circumstances and impact on efficacy and safety; (3) but only in vivo studies can provide key information on neointimal closure following implantation that will differentiate success from failure. CONCLUSIONS: A nomenclature is necessary to determine the optimal or suboptimal conditions for FDs and to design the virtual, in vitro and in vivo studies that will allow a better understanding of the factors involved in the success or failure of this novel treatment.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".