E-230 Comparison of PED and FRED flow diverters for posterior circulation aneurysms: a propensity-score matched cohort study
Bibliographic record
Abstract
Introduction/Purpose Flow diversion is a popular endovascular treatment for cerebral aneurysms, but studies comparing different types of flow diverters are scarce. Here, we performed a propensity score-matched cohort study comparing the Pipeline Embolization Device (PED) and Flow Redirection Intraluminal Device (FRED) for posterior circulation aneurysms. Materials and Methods Consecutive aneurysms of the posterior circulation treated at 25 neurovascular centers with either PED or FRED were collected. Propensity score matching was used to control for age, duration of follow-up imaging, adjunctive coiling, and aneurysm location, size, and morphology; previously ruptured aneurysms were excluded. The two devices were compared for the following outcomes: procedural complications, aneurysm occlusion, and functional outcome. Results A total of 375 aneurysms of the posterior circulation were treated in 369 patients. The PED was used in 285 (77.2%) and FRED in 84 (22.8%) of procedures. Aneurysms treated with the PED were more commonly fusiform in morphology and larger in size compared to the FRED aneurysms. To account for these important differences, propensity score matching was performed resulting in 44 PED and FRED unruptured aneurysm pairs. There were no differences between the two devices in terms of occlusion status, functional outcome, and neurologic complications. Conclusion Comparative analysis of PED and FRED for the treatment of unruptured posterior circulation aneurysms did not identify significant differences in aneurysm occlusion status at last follow-up, functional outcome, or neurologic complications. Disclosures C. Griessenauer: None. A. Enriquez-Marulanda: None. S. Xiang: None. T. Hong: None. H. Zhang: None. P. Taussky: None. R. Grandhi: None. M. Waqas: None. V. Tutino: None. A. Siddiqui: None. E. Levy: None. C. Ogilvy: None. A. Thomas: None. C. Ulfert: None. M. Möhlenbruch: None. L. Renieri: None. N. Limbucci: None. C. Parra-Fariñas: None. J. Burkhardt: None. P. Kan: None. L. Rinaldo: None. G. Lanzino: None. W. Brinjikji: None. E. Müller-Thies-Broussalis: None. M. Killer-Oberpfalzer: None. C. Islak: None. N. Kocer: None. M. Sonnberger: None. T. Engelhorn: None. M. Ghuman: None. V. Yang: None. A. Salehani: None. M. Harrigan: None. I. Radovanovic: None. A. Dmytriw: None.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".