Abstract WMP31: Vessel Wall Enhancement is Associated With Clinical and Imaging Markers of Aneurysm Instability
Bibliographic record
Abstract
Objective: It remains unclear whether patients with unruptured intracranial aneurysms (UICAs) should be treated. Vessel wall enhancement (VWE) in vessel wall MRI is emerging as a useful biomarker of aneurysm instability. We sought to evaluate whether VWE correlates with clinical and radiological markers of aneurysm instability in patients with UICAs. Methods: We conducted a retrospective analysis of a prospective cohort of patients with UICAs imaged with vessel wall MRI. Two blinded reviewers evaluated the presence of VWE. Univariate and multivariate logistic regression modelling was utilized to assess for association between VWE and (1) presence of cranial nerve palsy (CNP) and thunderclap headache on presentation, two well-established clinical markers of aneurysm instability; and (2) aneurysm size, a well-established radiological marker of aneurysm instability. Results: 94 patients with UICAs were included in the analysis; of these, 34 (36%) had VWE, 60 (64%) did not, 10 (11%) had CNP, 14 (15%) presented with thunderclap headache, and 40 (43%) had aneurysms >7mm in size. Inter-rater reliability for VWE ascertainment was excellent (kappa 0.86, 95%CI 0.75-0.97). 9 out of 10 patients (90%) with CNP had VWE (association testing not possible due to sparse counts). In multivariable analysis, thunderclap headache (OR 8.55, 95%CI 1.89-45.64; p=0.007) and aneurysm size (1.26, 95%CI 1.10-1.50; p=0.003) were independently associated with VWE. Conclusions: VWE independently associates with clinical and radiological markers of aneurysm instability. Prospective studies are needed to evaluate the clinical utility of this imaging biomarker.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".