Highest Lesion Growth Rates in Patients With Hyperacute Stroke
Bibliographic record
Abstract
Background and Purpose— The early growth of ischemic lesions has been described as being nonlinear, with lesion growth rates at their highest during the earliest period after stroke onset. We hypothesized that the time gap from imaging to revascularization results in higher lesion growth in patients with hyperacute presentation. Methods— Fifty-one patients with ischemic stroke with initial multimodal computed tomography (CT), follow-up CT after 24 hours, and successful endovascular recanalization were included and separated into 2 groups according to their median time from symptom onset to imaging (eg, hyperacute versus acute). The difference in Alberta Stroke Program Early CT Score (ASPECTS) between initial CT and follow-up CT was assessed, as well as volumetric lesion growth from early ischemic core in admission perfusion CT and total lesion volume in follow-up CT. Results— The median time from onset to imaging was 1.85 hours. There was no significant difference in admission ASPECTS (mean, 8.5 versus 8.2) or time from imaging to recanalization in both groups (median, 2.7 versus 2.4 hours; P =0.4). The mean (SD) lesion growth assessed by ASPECTS difference was 2.7 (2.3) in the hyperacute group and 1.6 (1.3) in the acute group ( P =0.03). The mean (SD) volumetric difference in the hyperacute group was 26.6 mL (43.2 mL) and 17.2 mL (26.3 mL; P =0.36) in the acute group, respectively. For every passing hour after onset, ASPECTS lesion growth was reduced by 0.4. Conclusions— Patients in the hyperacute phase showed increased ASPECTS lesion growth from imaging to recanalization suggesting a particular benefit of faster recanalization times in this group of patients with stroke.
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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 | 0.001 |
| 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".