The ACORNS grading scale: a novel tool for the prediction of malignant brain edema after endovascular thrombectomy
Bibliographic record
Abstract
BACKGROUND: To develop and validate a novel tool for predicting the development of malignant brain edema (MBE) in large vessel occlusion stroke patients after endovascular thrombectomy (EVT). METHODS: We used a prospectively registered population of EVT patients from three comprehensive stroke centers. The population was randomly divided into two subsets (7:3): a training cohort and an internal validation cohort. External validation was performed using the Endovascular Treatment for Acute Anterior Circulation Ischemic Stroke Registry in China (ACTUAL) database. MBE was defined as (1) hypodense parenchyma in at least 50% of the middle cerebral artery and signs of local brain swelling, and (2) a midline shift of ≥5 mm at the septum pellucidum or pineal gland with obliteration of the basal cisterns. The model was constructed using logistic regression analysis. The performance of the model was examined in terms of discrimination and calibration. RESULTS: After adjusting for other confounders, baseline National Institutes of Health Stroke Scale (NIHSS) and Alberta Stroke Program Early CT (ASPECT) scores, a clinical history of hypertension, collateral status, intravenous thrombolysis before thrombectomy, fasting blood glucose, reperfusion status, and occlusion site were found to be independent predictors of MBE. These variables were combined to create the ACORNS grading scale. The areas under the curve in receiver operating curve analysis were 0.850 (95% CI 0.816 to 0.884), 0.874 (95% CI 0.821 to 0.926), and 0.785 (95% CI 0.740 to 0.829) for the training, internal validation, and external validation cohorts, respectively, indicating good discriminative performance in the validation cohorts. CONCLUSIONS: The ACORNS grading scale is an accurate and easily applicable model for the prediction of the development of MBE after EVT.
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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 | 0.011 |
| 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 | 0.000 |
| 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".