Different Scores Predict the Value of Hemorrhagic Transformation after Intravenous Thrombolysis in Patients with Acute Ischemic Stroke
Post-publication record
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Bibliographic record
Abstract
Objective. To evaluate the value of the Alberta stroke project early CT score (ASPECTS), DRAGON score, SEDAN score, and HAT score in predicting hemorrhagic transformation (HT) after intravenous thrombolysis in patients with acute ischemic stroke (AIS). Methods. The clinical data of 248 AIS patients treated with intravenous thrombolysis in our hospital from December 2017 to December 2019 were analyzed. According to the prognosis, all patients were divided into the non-HT group (n = 200) and the HT group (n = 48). Univariate analysis and multivariate logistic regression models were used to analyze clinical data to determine the influencing factors of HT after intravenous thrombolysis in AIS patients. The receiver operating characteristic curve was used to evaluate the ASPECTS, DRAGON, SEDAN, and HAT scores to the value of predicting HT after intravenous thrombolysis in AIS patients. Results. The lower the ASPECTS score and the higher the DRAGON, SEDAN, and HAT scores, the higher the incidence of HT after intravenous thrombolysis in AIS patients ( P < 0.05 ). The results of multivariate logistic regression analysis showed that the patient’s age, atrial fibrillation, baseline NIHSS score, early signs of infarction on admission with head CT, time from onset to thrombolytic therapy, and thrombolytic drugs were all independent factors affecting intravenous thrombolysis in AIS patients ( P < 0.05 ). The area under the curve (AUC) of the predictive value of ASPECTS for HT is 0.895 (95% CI 0.813–0.977). When the optimal cutoff value is 0.607, the sensitivity is 100% and the specificity is 60.7%. The AUC of the predictive value of DRAGON for HT is 0.877 (95% CI 0.790–0.964). When the optimal cutoff value is 0.665, the sensitivity is 84.4% and the specificity is 82.1%. The AUC of the predictive value of SEDAN for HT is 0.764 (95% CI 0.638–0.890). When the optimal cutoff value is 0.474, the sensitivity is 78.6% and the specificity is 68.8%. The AUC of the predictive value of HAT for HT is 0.777 (95% CI 0.651–0.903). When the optimal cutoff value is 0.509, the sensitivity is 68.8% and the specificity is 82.1%. Conclusion. The lower the ASPECTS score and the higher the DRAGON, SEDAN, and HAT scores, the higher the incidence of HT in AIS patients. The patient’s age, atrial fibrillation, baseline NIHSS score, early signs of infarction on admission with head CT, time from onset to thrombolytic therapy, and thrombolytic drugs are all independent factors affecting HT in AIS patients. The scores of ASPECTS, DRAGON, SEDAN and HAT have certain value in predicting HT after intravenous thrombolysis in AIS patients, and the predicted value of ASPECTS score is the best.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".