Risk Assessment of Symptomatic Intracerebral Hemorrhage After Thrombolysis Using DWI-ASPECTS
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Pretreatment lesion size on diffusion-weighted imaging (DWI) is a risk factor for symptomatic intracerebral hemorrhage (sICH) associated with thrombolytic treatment. Here, we investigated whether the Alberta Stroke Programme Early CT Score (ASPECTS) applied to DWI images (DWI-ASPECTS) predicts sICH risk accurately. METHODS: In this retrospective multicenter study, prospectively collected data of 217 patients with anterior circulation stroke treated with intravenous or intraarterial thrombolysis within 6 hours after symptom onset were analyzed. Pretreatment DWI-ASPECTS scores were assessed by 2 independent investigators. For bleeding risk analysis, DWI-ASPECTS scores were either categorized into 0 to 7 (n=105) or 8 to 10 (n=112) or in 3 groups of similar sample size (DWI-ASPECTS 0 to 5 [n=69], 6 to 7 [n=70], and 8 to 10 [n=78]). RESULTS: DWI-ASPECTS scores correlated well with the DWI lesion volume (r=0.77, P<0.001, Spearman Rank test). Interobserver reliability for the assessment of DWI-ASPECTS was moderate (weighted kappa 0.441 [95% CI 0.373 to 0.509]). Twenty-three (10.6%) patients developed sICH. The sICH rate was significantly higher in patients with DWI-ASPECTS scores 0 to 7 (n=21, 15.1%) as compared to patients with DWI-ASPECTS scores 8 to 10 (n=2, 2.6%, P=0.004). sICH risk was 20.3%, 10%, and 2.6% in the 0 to 5, 6 to 7, and 8 to 10 DWI-ASPECTS groups, respectively. DWI-ASPECTS remained an independent prognostic factor for sICH after adjustment for clinical baseline variables (age, NIHSS, time to thrombolysis). CONCLUSIONS: DWI-ASPECTS predicts sICH risk after thrombolysis and may be helpful to contributing to quick sICH risk assessment before thrombolytic therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".