Hemodynamics derived from computational fluid dynamics based on magnetic resonance angiography is associated with functional outcomes in atherosclerotic middle cerebral artery stenosis
Bibliographic record
Abstract
Background: To investigate the relationship between fluid-attenuated inversion recovery (FLAIR) vascular hyperintensity (FVH), hemodynamics, and functional outcome in atherosclerotic middle cerebral artery (MCA) stenosis using a computational fluid dynamics (CFD) model based on magnetic resonance angiography (MRA), according to a modified Rankin Scale (mRS) at 3 months. Methods: A total of 120 patients with 50–99% atherosclerotic MCA stenosis were included. The training and internal validation groups were composed of 99 participants and 21 participants, respectively. Demographic, imaging data, and functional outcome (mRS at 3 months) were collected. Hemodynamic parameters were obtained from the CFD model. The FVH score was based on the number of territories where FVH is positive, according to the spatial distribution in the Alberta Stroke Program Early Computed Tomography Score (ASPECTS). The prediction models were constructed according to clinical and hemodynamic parameters using multivariate logistic analysis. The DeLong test compared areas under the curves (AUCs) of the models. Results: The multivariable logistic regression analysis showed that the National Institute of Health Stroke Scale (NIHSS) at admission, hypertension, hyperlipidemia, the ratio of wall shear stress before treatment (WSSRbefore), and difference in the ratio of wall shear stress (WSSR) were independently associated with functional outcome (all P<0.05). In the training group before treatment, the AUC of model 1a (only clinical variables) and 2a (clinical variables with addition of WSSRbefore) were 0.750 and 0.802. After treatment, the AUC of model 1b (only clinical variables) and 2b (clinical variables with addition of difference in WSSR) were 0.815 and 0.883, respectively. The AUC of models with hemodynamic parameters was significantly higher than the models based on clinical variables only (all P<0.05, DeLong test). In the internal validation group before treatment, the AUC of the model (clinical variables) was 0.782, and that of the model (clinical variables and WSSRbefore) was 0.800. After treatment, the AUC of the model (clinical variables) was 0.833, and that of the model (clinical variables and difference in WSSR) was 0.861. There were no significant differences between the good and the poor functional outcome group concerning FVHbefore scores before treatment (0.30±0.81 vs. 0.26±0.97; P=0.321) and FVHafter scores after treatment (0.08±0.39 vs. 0.00±0.00; P=0.244). Conclusions: Hemodynamics was associated with functional outcomes in patients with ischemic stroke attributed to atherosclerotic MCA stenosis, while FVH was not. Hemodynamic parameters were of great importance in the prediction models.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".