Inter- and intraobserver variability in the assessment of brain arteriovenous malformation angioarchitecture and endovascular treatment results.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Several angiographic features of brain arteriovenous malformations (BAVMs) have been associated with an increased risk of hemorrhage. However, interpretation of these features may not be consistent between observers. We conducted a study to determine inter- and intraobserver agreement of various angioarchitectural characteristics of BAVM. MATERIALS AND METHODS: Two experienced interventional neuroradiologists independently reviewed pre- and post-endovascular treatment angiograms from 50 consecutive patients. Axial CT and/or MR images before treatment were included. We collected the following data: Spetzler-Martin grades, number of involved arterial territories, associated aneurysms by location (circle of Willis, feeding artery, intranidal, and venous), and nidus reduction after endovascular treatment (<33%, 33%-66%, and >66%). The reviewers were compared with each other, and 1 was compared with himself after a 3-month interval. Measures of agreement were performed by using the kappa statistic (kappa) for nominal data and the weighted kappa for ordinal data. RESULTS: Inter- and intraobserver agreement were higher for assessment of the Spetzler-Martin grade (weighted kappa = 0.70/0.75) and nidus size reduction after endovascular treatment (kappa = 0.74/0.77). Inter- and intraobserver agreement were inferior for findings concerning feeding artery aneurysms (kappa = 0.19/0.36), intranidal aneurysms (kappa = 0.34/0.35), and venous aneurysms (kappa = 0.50/0.67). CONCLUSION: Angiographic characteristics of BAVMs considered as risk factors for hemorrhage, such as aneurysms, are not reliably detected on global angiograms between different observers. In contrast, the Spetzler-Martin grading system and angiographic results of endovascular treatment can be used with high observer agreement.
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 | 0.047 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".