Abstract TP245: Prediction of Hemorrhagic Transformation in Patients With Acute Ischaemic Stroke and Atrial Fibrillation With Ct Perfusion Aspects Scores
Bibliographic record
Abstract
Background and Purpose: Hemorrhage transformation (HT) is common in patients with acute cerebral infarction caused by atrial fibrillation. The prediction of HT is crucial after acute stroke, especially for the patients received vessel recanalization therapy. The Alberta Stroke program early CT score (ASPECTS) is used to estimate early ischemic changes within the MCA territory in the acute stroke setting. Several studies indicated that CT perfusion (CTP) and MR diffusion weighted imaging (DWI) ASPECTS scores was useful to quantify the degree of ischemic brain tissue. Hereby we did the study to explore the association of CT perfusion ASPECTS scores with HT in patients with acute ischemic stroke and atrial fibrillation. Methods: This was a single center retrospective study. All patients with middle cerebral artery infarction and atrial fibrillation from September 2008 to September 2013 were included. MR imaging including DWI and gradient echo sequence (GRE), and CTP were required to identify the HT and determine the scores of CTP- ASPECTS. Demographic and clinical characteristics of the HT positive and negative groups were explored. Results: Fifty-four patients were analyzed, among them twenty-four patients (44%) developed HT. According to logistic regression analysis, mean transit time (MTT), cerebral blood volume (CBV) and DWI-ASPECTS scores were associated with HT ( p = 0.035, 0.044, and 0.020 respectively). The following receiver operating characteristics (ROC) analysis revealed area under the curve of MTT, CBV, CBF and DWI were 0.588, 0.737, 0.687, and 0.841 respectively. CBV-ASPECTS score was found to have medium prediction value of HT among all CTP-ASPECTS parameters. ROC analysis also indicated that CBV-ASPECTS score < 7 was the optimal threshold. Conclusions: CTP-ASPECTS was useful to predict the HT of acute ischemic stroke caused by atrial fibrillation and CBV-ASPECTS score < 7 was the preferable parameter.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".