Development and validation of a nomogram to predict symptomatic intracranial hemorrhage following endovascular treatment in acute ischemic stroke: A single center retrospective, observational study
Bibliographic record
Abstract
Symptomatic intracranial hemorrhage (SICH) is a severe post-endovascular treatment (EVT) complication of acute ischemic stroke, leading to high morbidity, mortality, and neurological deficits. However, despite these risk factors, there is currently no clinically accepted predictive model to predict SICH risk in EVT patients. This study aimed to identify independent perioperative risk factors for SICH and to provide a new nomogram-based predictive model for large vessel occlusion patients. This study retrospectively examined 127 acute ischemic stroke patients receiving EVT from the First Affiliated Hospital of Wenzhou Medical University. Inclusion criteria included the National Institutes of Health Stroke Scale (NIHSS), Alberta Stroke Program Early CT Score (ASPECTS), and American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR) collateral scores. Using these predictors, a logistic regression model was used to generate the NIHSS/ASPECTS/ASITN (NAA) nomogram. The predictive ability was measured with the area under the receiver operating characteristic curve and calibration plots. Our data demonstrated that SICH patients had significantly higher baseline NIHSS scores (median: 22.38 vs 15.03; P < .001), ASPECTS (median: 5.89 vs 7.69; P < .001), and ASITN/SIR scores (median: 1.62 vs 2.69; P < .001). The NAA nomogram exhibited an area under the receiver operating characteristic curve value of 0.845 (95% confidence interval: 0.763-0.928), which is superior to the predictive performance. Calibration plots were robust in predicting and observing values. The NAA nomogram is a robust predictor of SICH after EVT that is more accurate than any scoring system. More external validation is needed to make it generalizable to diverse clinical settings.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".