Alberta stroke programme early CT score on diffusion-weighted imaging and clot burden scoring on MR angiography in the prediction of hemorrhagic transformation after thrombolysis in acute cerebral infarction
Bibliographic record
Abstract
Objective To evaluate Alberta stroke programme early CT score on diffusion-weighted imaging (DWI-ASPECTS)and clot burden score on MR angiography (MRA-CBS)in predicting hemorrhagic transformation(HT) in acute anterior circulation cerebral infarction after thrombolysis in diffusion-weighted imaging Alberta stroke program.Methods A total of 37 consecutive patients with acute anterior circulation cerebral infarction were treated with thrombolysis.The clinical information , score of DWI-ASPECTS before thrombolysis , score of MRA-CBS before thrombolysis and images of enhanced gradient echo T 2*-weighted angiographywithin ( ESWAN) 24 hours before and after thrombolysis were all collected.The interval between onset and the two MRI scans were recorded respectively.We identified HT according to the images of ESWAN scanned after thrombolysis , and divided patients into 2 groups:with HT(14 cases) and without HT (23 cases).Differences of clinical data and imaging indicators between the two groups were compared by using Fisher′s exact test and Wilcoxon rank sum test.Logistic regression analysis was performed by taking HT as the dependent variable , and the scores of NIHSS , DWI-ASPECTS and MRA-CBS at admission were taken as independent variables.The variables which were statistically significant in logistic regression analysis were enrolled in receiver operating characteristic analysis.Results In HT group, the scores of NIHSS, DWI-ASPECTS and MRA-CBS were 15.00 ±5.30, 6.00(4.75,7.00) and 7.00(0.75,8.50) respectively.In the other group without HT, these scores were 7.00 ±4.80, 9.00(8.00,10.00)and 10.00(6.00,10.00) respectively.Compared with patients without HT , patients with HT had a higher baseline NIHSS score ( Z=-3.72,P<0.01), a lower DWI-ASPECTS (Z=-4.13,P<0.01) and a lower MRA-CBS (Z=-2.00, P<0.05).Logistic regression analysis showed that the scores of DWI-ASPECTS ( OR 0.42,95%CI 0.21-0.87,P <0.05 ) and NIHSS ( OR 1.22, 95%CI 1.00-1.48, P <0.05 ) at baseline predicted HT development independently.Receiver operating characteristic analysis showed that the optimal cut -off point of DWI-ASPECTS to predict the development of HT was≤7.Its sensitivity, specificity and area under ROC curve were 92.9%, 78.3% and 0.902 respectively ( P<0.01 ).Conclusions ASPECTS on DWI is of great value in predicting HT after thrombolysis in acute cerebral infarction.CBS on MRA can provide additional information for predicting HT. Key words: Stroke ; Magnetic resonance imaging ; Hemorrhage ;
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".