Cerebral blood volume Alberta Stroke Program Early Computed Tomography Score predicts intracranial hemorrhage after thrombectomy in patients with acute ischemic stroke in an extended time window
Bibliographic record
Abstract
Background Higher baseline Alberta Stroke Program Early Computed Tomography Score (ASPECTS) was associated with a lower probability of hemorrhagic transformation in patients with acute ischemic stroke (AIS). Purpose To investigate the predictive value of cerebral blood volume (CBV)-ASPECTS of intracranial hemorrhage (ICH) in AIS treated with thrombectomy selected by computed tomographic perfusion (CTP) in an extended time window. Material and Methods A total of 91 consecutive patients with AIS with large vessel occlusion in the anterior circulation after thrombectomy in an extended time window were enrolled between January 2018 and September 2019. ICH was diagnosed according to Heidelberg Bleeding Classification. CBV-ASPECTS was assessed by evaluating each ASPECTS region for relatively low CBV value compared with the mirror region in the contralateral hemisphere. Demographic characteristics, clinical data, CBV-ASPECTS, and procedure process and results were compared between patients with ICH and those without. Results ICH occurred in 31/91 (34.1%) patients with AIS. Symptomatic ICH (sICH) was observed in 4 (4.4%) patients, while asymptomatic ICH (aICH) was seen in 27 (29.7%). In univariate analysis, both ICH and aICH were associated with high admission NIHSS score ( P<0.001 and P<0.001, respectively), more passes of retriever ( P = 0.007 and P = 0.019, respectively), low NCCT-ASPECTS ( P = 0.013 and P = 0.034, respectively), and low CBV-ASPECTS ( P < 0.001 and P < 0.001, respectively). After multivariable analysis, low CBV-ASPECTS remained an independent predictor of ICH (odds ratio [OR] 0.521, 95% confidence interval [CI] 0.371–0.732, P < 0.001) and aICH (OR 0.532, 95% CI 0.376–0.752, P < 0.001), respectively. Conclusion Low CBV-ASPECTS independently predicts ICH in patients with AIS treated with thrombectomy selected by CTP in an extended time window.
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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 | 0.001 |
| 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) | 0.001 | 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".