Abstract TP79: Do Early Ischemic Changes Occur on CT Within the First Hour of Acute Ischemic Stroke
Bibliographic record
Abstract
Introduction: Early ischemic changes (EIC) on non-contrast computed tomography (NCCT) can appear within 6 hours of last known normal (LKN), and can be quantified using the Alberta Stroke Program Early CT Score (ASPECTS). However, there is lack of data describing when EIC first appear. We leveraged our Mobile Stroke Unit (MSU) to determine the incidence of EIC on NCCT within 1 hour of LKN. Methods: Prospectively derived data were analyzed from patients on our MSU who were independently adjudicated as tissue plasminogen activator (tPA) eligible, had NCCT within 1 hour of LKN, and had definite strokes based on subsequent testing. EIC, defined as ASPECTS ≤ 7, was measured and correlated to time from LKN, stroke severity (National Institutes of Health Stroke Scale, NIHSS), and presence of large vessel occlusion (LVO) on neuroimaging. All scans were obtained on an 8 slice Ceretom (Neurologica Corp) and graded by a Vascular Neurology fellow, with random scans compared with a Vascular Neurology attending (κ=0.69). Results: 80 tPA eligible patients with NCCT within 1 hour of LKN were identified. 57 had definite strokes and/or strokes reversed by tPA. Of these, 54 (95%) had NCCT with sufficient diagnostic quality. Mean ASPECTS was 9.2 (median 10, interquartile range (IQR) 9-10) with a mean of 45.3 minutes (median 46, IQR 39-52) from LKN. Average NIHSS was 14.9. EIC (e.g. ASPECTS 6, 6, 7, 7) was identified in 4 patients (7%). There was no association between ASPECTS and time from LKN to CT (p=0.63), stroke severity (p=0.12) or presence of LVO (p= 0.09); the LVO analysis was limited by the small number of EIC patients (n=4). Conclusions: Based on our experience, EIC may be present but ASPECTS is not < 6 within the first hour after LKN. Close scrutiny of NCCT for EIC within this timeframe may not be necessary for determining eligibility for tPA or endovascular thrombectomy.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".