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 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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".