Technical Risk Stratification Nomogram Model for 90‐Day Mortality Prediction in Patients With Acute Basilar Artery Occlusion Undergoing Endovascular Thrombectomy: A Multicenter Cohort Study
Bibliographic record
Abstract
BACKGROUND: This study aimed to establish and validate a nomogram model for predicting 90-day mortality in patients with acute basilar artery occlusion receiving endovascular thrombectomy. METHODS AND RESULTS: A total of 242 patients with basilar artery occlusion undergoing endovascular thrombectomy were enrolled in our study, in which 172 patients from 3 stroke centers were assigned to the training cohort, and 70 patients from another center were assigned to the validation cohort. Univariate and multivariate logistic regression analyses were adopted to screen prognostic predictors, and those with significance were subjected to establish a nomogram model in the training cohort. The discriminative accuracy, calibration, and clinical usefulness of the nomogram model was verified in the internal and external cohorts. Six variables, including age, baseline National Institutes of Health Stroke Scale score, Posterior Circulation-Alberta Stroke Program Early CT (Computed Tomography) score, Basilar Artery on Computed Tomography Angiography score, recanalization failure, and symptomatic intracranial hemorrhage, were identified as independent predictors of 90-day mortality of patients with basilar artery occlusion and were subjected to develop a nomogram model. The nomogram model exhibited good discrimination, calibration, and clinical usefulness in both the internal and the external cohorts. Additionally, patients were divided into low-, moderate-, and high-risk groups based on the risk-stratified nomogram model. CONCLUSIONS: Our study proposed a novel nomogram model that could effectively predict 90-day mortality of patients with basilar artery occlusion after endovascular thrombectomy and stratify patients with high, moderate, or low risk, which has a potential to facilitate prognostic judgment and clinical management of stroke.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".