P.193 Competitive Flow Diversion: Proposed Classification System
Bibliographic record
Abstract
Background: Competitive flow diversion (CFD) is a novel application of flow diversion stenting (FDS), redirecting flow into a normal artery proximal or distal to the aneurysmal parent artery. A classification system for CFD has not been previously reported. Methods: Report of operative technique and novel classification system for CFD. Results: A patient with subarachnoid haemorrhage and three aneurysms arising from the Pcomm-P1 complex, was treated with endovascular coiling and CFD. The PCOM aneurysm was coiled. Two aneurysms arose from the distal right P1- PCA. After a failed attempt to treat with FDS across the P1-PCA, the P1-aneurysms were successfully treated with CFD distal to the P1-PCA, from Pcomm to P2. Over 12 months, CFD redirected flow via ICA-Pcomm-P2, reducing the size of the P1-PCA, obliterating the P1-aneurysms. Herein, we classify competitive flow diversion into two types. Type I CFD is when the parent artery harbouring the aneurysm is “jailed” proximally. Type II CFD occurs when flow is diverted from the parent artery distal to the aneurysm origin. Conclusions: Herein, we propose a novel classification for CFD. We describe the first case of aneurysm occlusion in the circle of Willis with Type II CFD, and use of CFD for the treatment of multiple adjacent aneurysms.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".