A Simple Flow Classification Parameter Can Discriminate Rupture Status in Intracranial Aneurysms
Bibliographic record
Abstract
BACKGROUND: A simple dimensionless aneurysm number ($An$), which depends on geometry and flow pulsatility, was previously shown to distinguish the flow mode in intracranial aneurysms (IA): vortex mode with a dynamic vortex formation/evolution if $An > 1$, and cavity mode with a steady shear layer if $An < 1$. OBJECTIVE: To hypothesize that $An\ > \ 1$ can distinguish rupture status because vortex mode is associated with high oscillatory shear index, which, in turn, is statistically associated with rupture. METHODS: The above hypothesis is tested on a retrospective, consecutively collected database of 204 patient-specific IAs. The first 119 cases are assigned to training and the remainder to testing dataset. $An$ is calculated based on the pulsatility index (PI) approximated either from the literature or solving an optimization problem (denoted as$\ \widehat {PI}$). Student's t-test and logistic regression (LR) are used for hypothesis testing and data fitting, respectively. RESULTS: $An$ can significantly discriminate ruptured and unruptured status with 95% confidence level (P < .0001). $An$ (using PI) and $\widehat {An}$ (using $\widehat {PI}$) significantly predict the ruptured IAs (for training dataset $An\!:\ $AUC = 0.85, $\widehat {An}\!:\ $AUC = 0.90, and for testing dataset $An\!:\ $sensitivity = 94%, specificity = 33%, $\widehat {An}\!:\ $sensitivity = 93.1%, specificity = 52.85%). CONCLUSION: $An > 1$ predicts ruptured status. Unlike traditional hemodynamic parameters such as wall shear stress and oscillatory shear index, $An$ has a physical threshold of one (does not depend on statistical analysis) and does not require time-consuming flow simulations. Therefore, $An$ is a simple, practical discriminator of IA rupture status.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".