Evaluation of flow diverters for cerebral aneurysm therapy: recommendations for imaging analyses in clinical studies, endorsed by ESMINT, ESNR, OCIN, SILAN, SNIS, and WFITN
Bibliographic record
Abstract
BACKGROUND: Multiple studies and meta-analyses have described the technical and clinical outcomes in large cohorts of aneurysm patients treated with flow diverters (FDs). Variations in evaluation methodology complicate making comparisons among studies, hinder understanding of the device behavior, and pose an obstacle in the assessment of further advances in FD therapy. METHODS: A multidisciplinary panel of neurointerventionalists, imaging experts, and neuroradiologists convened with the goal of establishing consensus recommendations for the standardization of image analyses in FD studies. RESULTS: A standardized methodology is proposed for evaluating and reporting radiological outcomes of FD treatment of intracranial aneurysms. The recommendations include general imaging considerations for clinical studies and evaluations of longitudinal changes, such as neointimal lining and stenosis. They cover standards for classification of aneurysm location, morphology, measurements, as well as the assessment of aneurysm occlusion, wall apposition, and neck coverage. These reporting standards further define four specific braid deformation patterns: foreshortening, fish-mouthing, braid bump deformation, and braid collapse, collectively termed 'F2B2'. CONCLUSIONS: When widely applied, standardization of methods of measuring and reporting outcomes will help to harmonize the assessment of treatment outcomes in clinical studies, help facilitate communication of results among specialists, and help enable research and development to focus on specific aspects of FD techniques and technology.
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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 | 0.380 | 0.482 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.015 | 0.007 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".