A Simple Grading Scale for Predicting Symptomatic Intracranial Hemorrhage after Mechanical Thrombectomy
Bibliographic record
Abstract
INTRODUCTION: Hemorrhagic transformation, especially symptomatic intracranial hemorrhage (sICH), is a common complication after mechanical embolectomy. This study explored a grading scale based on clinical and radiological parameters to predict sICH after mechanical embolectomy. METHODS: Demographic and clinical data were retrospectively collected from patients with acute ischemic stroke treated with mechanical embolectomy at West China Hospital. Clinical and radiological factors associated with sICH were identified and used to develop the "STBA" grading scale. This score was then validated using data from an independent sample at the First Affiliated Hospital of Kunming Medical University. RESULTS: We analyzed 268 patients with acute ischemic stroke who were treated with mechanical embolectomy at West China Hospital, of whom 30 (11.2%) had sICH. Patients were rated on an "STBA" score ranging from 0 to 6 based on whether systolic blood pressure was ≥145 mm Hg at admission (yes = 2 points; no = 0 points), time from acute ischemic stroke until groin puncture was ≥300 min (yes = 1; no = 0), blood glucose was ≥8.8 mmol/L (yes = 1; no = 0), and the Alberta Stroke Program Early Computed Tomography score at admission was 0-5 (2 points), 6-7 (1 point), or 8-10 (0 points). The STBA score showed good discrimination in the derivation sample (area under the receiver operating characteristic curve = 0.858) and in the validation sample (area = 0.814). CONCLUSIONS: The STBA score may be a reliable clinical scoring system to predict sICH in acute ischemic stroke patients treated with mechanical embolectomy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".