A nomogram for predicting malignant cerebral artery infarction in the modern thrombectomy era
Bibliographic record
Abstract
Objective This study aimed to develop and validate a nomogram to predict malignant cerebral artery infarction (MMI) after endovascular treatment (EVT) in patients with acute ischemic stroke (AIS) in the modern thrombectomy era. Methods We retrospectively analyzed data from a prospective cohort of consecutive patients with AIS who underwent EVT at Xuanwu hospital between January 2013 and June 2021. A multivariable logistic regression model was employed to construct the nomogram for predicting MMI after EVT. The discrimination and calibration of the nomogram were assessed both in the derivation and validation cohorts. Results A total of 605 patients were enrolled in this study, with 425 in the derivation cohort and 180 in the validation cohort. The nomogram was developed based on admission systolic blood pressure (SBP), the National Institute of Health Stroke Score (NIHSS), the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), vessel occlusion site, EVT time window, and recanalization status. The nomogram displayed good discrimination with the area under the receiver operating characteristics (ROCs) curve (AUC) of 0.783 [95% confidence interval (CI), 0.726–0.840] in the derivation cohort and 0.806 (95% CI, 0.738–0.874) in the validation cohort. The calibration of the nomogram was good as well, with the Hosmer–Lemeshow test of p = 0.857 in the derivation cohort and p = 0.275 in the validation cohort. Conclusion In the modern thrombectomy era, a nomogram containing admission SBP, NIHSS, ASPECTS, vessel occlusion site, EVT time window, and recanalization status may predict the risk of MMI after EVT in patients with AIS.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".