Size Ratio: A Morphological Factor Predictive of the Rupture of Cerebral Aneurysm?
Bibliographic record
Abstract
BACKGROUND: Determining factors predictive of the natural risk of rupture of cerebral aneurysms is difficult. We studied morphological factors associated with rupture in a study model of patients with mirror location intracranial aneurysms, one aneurysm that had ruptured and one that had not, each patient served as their own control attempting to eliminate confounding variables. METHODS: We collected five one-dimensional measurements and four two-dimensional indices from three-dimensional rotational digital subtraction angiography images of patients in the proposed study model and explored their correlation with aneurysm rupture. Parameters were analyzed with a paired Student's t test for significance and significant parameters were further examined by multivariate conditional logistic regression analysis. RESULTS: Fifty-two patients with 52 pairs of intracranial aneurysms in a mirror location were studied. The maximum perpendicular height, neck diameter, maximum width, maximum height, aspect ratio, size ratio, and bottleneck factor were significantly associated with ruptured aneurysms on bivariate analysis. A logistic regression analysis showed that only size ratio, which was defined as the ratio of the maximal height to parent artery average diameter, is independently correlated with ruptured intracranial aneurysms. CONCLUSIONS: In a case-control study of patients with mirror location intracranial aneurysms, size ratio was identified as the unique morphological factor associate with the rupture of cerebral aneurysms.
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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 | 0.005 |
| 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 | 0.000 | 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".