Abstract WP131: A Proposed Weighted-ASPECTS Based on the NIH Stroke Scale
Bibliographic record
Abstract
The Alberta Stroke Program Early CT score (ASPECTS) is a 10-item score of the territory of the middle cerebral artery (MCA). Helpful in defining the extent of ischemia, prognosis, and neurological severity, the role of each item in the score is not clear in the literature. Variables such as age, cerebral hemisphere, vascular occlusion, and collateral status seem to influence the role of each item in terms of neurological severity measured by the NIH Stroke Scale (NIHSS). Recognizing the clinical role of these items can be useful for accurately measuring the clinical-radiological mismatch and maybe allow for expanded therapeutic windows in stroke. We aim to identify the correlation of each ASPECTS item with neurological severity. In an ambispective analysis, patients with ischemic Stroke from a national network of hospitals were included. We conducted a linear regression model, with the NIHSS scores at admission serving as the dependent variable. Models were adjusted for age, time from stroke symptom onset and hemisphere involvement. We excluded patients with an ASPECTS of 10 and those without an arterial-CT brain scan at admission. In total, 587 patients from 32 hospitals were included in the final analysis. Males represented 39.5% of the sample, with a mean age of 68 (±16) years. The median NIHSS score was 11 [5, 17], and the median ASPECTS was 8 [6, 9]. Among patients without occlusion of MCA 1, 2, and 3 (n=352), factors significantly associated with NIHSS scores included age (beta-coefficient 0.08), time from stroke onset (-0.001), left hemisphere (1.58), and ASPECTS subitems M1 (4.7), M3 (2.5), M4 (1.9), M5 (1.8), Insula (1.7), and Lentiform (2.8). In patients with proximal occlusion (n=235), factors significantly associated with NIHSS scores were age (0.07), left hemisphere (4.1), ASPECTS subitems M6 (2.4) and Lentiform (2.4), MCA 1 occlusion (3.3), and absent collateral status (5.8). Our results confirm the different roles of each ASPECTS item on neurological severity in acute stroke patients. Adjusted equations with confidence intervals were created to predict the NIHSS score. Our results bring to the literature an innovative tool for numerically assessing the clinical-radiological mismatch using a simple and accessible neuroimaging method.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".