Arterial spin labeling-ASPECTS and a conventional MRI-based nomogram for predicting prognosis after surgical revascularization in Moyamoya disease
Bibliographic record
Abstract
Objective This study aimed to explore the significant factors of prognosis in patients with Moyamoya disease (MMD) after surgical revascularization and to develop a nomogram model for predicting poor prognosis. Materials and methods We retrospectively analyzed magnetic resonance imaging (MRI) and clinical data of 128 patients with MMD. The patients were randomly assigned to training and validation cohorts in a ratio of 7:3. Multivariate logistic regression analysis was applied to identify factors significantly associated with prognosis. The predictive efficiencies of the models were evaluated using receiver operating characteristic (ROC) curves and compared using the Delong test. We then developed a nomogram model for prediction and verified it using a validation cohort. Results Preoperative arterial spin labeling (ASL)-Alberta Stroke Program Early computed tomography Score (ASL-ASPECTS), admission modified Rankin scale (mRS) score, ivy sign, and Houkin’s grade >2 were significantly associated with poor prognosis (mRS > 2). The areas under the curves (AUCs) for predicting poor prognosis were 0.772, 0.855, 0.899, and 0.994 for clinical, conventional MRI, ASL-based, and combination models, respectively. The results of the Delong test demonstrated the superior prediction ability of the combination model compared with the clinical, conventional MRI, and ASL models (all p < 0.001). Calibration curve analysis showed that the predictive probability of the nomogram model was highly consistent in the training cohort. The decision curve showed a net predictive benefit in the validation cohort. Conclusion Preoperative ASL-ASPECTS, admission mRS, ivy sign, and Houkin grade >2 were significantly associated with poor prognosis in patients with MMD after surgical revascularization. The nomogram model, including enrolled ASL-ASPECTS and MRI features, may help improve prognosis prediction.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".