Multimodal CT imaging characteristics may predict post-reperfusion infarct volume in wake-up stroke patients
Bibliographic record
Abstract
Background: Accurate prediction of subsequent infarct volume in acute ischemic stroke (AIS) patients helps determine appropriate interventions and prognosis. The objectives are to assess whether early multimodal CT imaging characteristics of wake-up stroke (WUS) patients could predict post-reperfusion infarct volume and evaluate the accuracy of baseline infarct and penumbra volumes for predicting follow-up infarct volume. Methods: This retrospective study included WUS patients, last seen well (LSW) >6 h, with multimodal CT imaging at baseline. Baseline non-contrast CT (NCCT) and CT perfusion were analyzed using RAPID software, and CT angiography using maximum intensity projection. Post-reperfusion infarct volume was assessed at 24-h following reperfusion on magnetic resonance diffusion-weighted imaging (DWI). Patients were stratified by treatment module for analyses. Results: Of 34 eligible patients, 9 (26.5%) received intravenous recombinant tissue plasminogen activator (r-tPA) alone and 25 (73.5%) received both endovascular thrombectomy (EVT) and r-tPA. All patients had a strong correlation between baseline NCCT alberta stroke program early CT score, clot burden score (CBS), Tan score, infarct volume, penumbra volume with 24-h post-reperfusion infarct volume (respectively, r=0.172, P=0.015; r=0.118, P=0.047; r=0.149, P=0.024; r=0.311, P=0.001 and r=0.120; P=0.045). Among reperfusion therapies, WUS patients who received EVT had a significantly lower 24-h post-reperfusion infarct volume and had a significant difference between baseline infarct volume and 24-h post-reperfusion infarct volume (respectively, 82 vs. 14, P=0.032 and 47 vs. 14, P=0.04). Conclusions: Primarily obtained multimodal CT imaging characteristics may predict post-reperfusion infarct volume in WUS patients, and those who underwent EVT had a significantly lower post-reperfusion infarct volume.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".