Early ischemic CT changes before thrombolysis: The influence of age and diabetes mellitus
Bibliographic record
Abstract
OBJECTIVES: The significance of early ischemic changes (EIC) on computed tomography (CT) within 3 hours after stroke onset remains controversial. The semi-quantitative Alberta Stroke Program Early CT Score (ASPECTS) is found to have prognostic value in early stroke. This study assesses factors associated with the presence of EIC and the relation between EIC and clinical outcome. MATERIALS AND METHODS: CT scans from 61 consecutive patients receiving thrombolytic therapy were reviewed by 3 experienced stroke neurologists, assessing EIC (ASPECTS) and vascular signs (hyperdense middle cerebral artery stem and/or branches). Short-term outcome was assessed with the National Institute of Health Stroke Scale at 24 hours and long-term outcome with the modified Rankin Scale score after 3 months. RESULTS: The prevalence of EIC was 54% and the agreement between assessors was good (kappa 0.52-0.67). EIC was independently associated with younger age and absence of diabetes mellitus. Neither EIC nor vascular signs were associated with 3-months outcome. CONCLUSIONS: ASPECTS is as simple, systematic approach to assessing EIC, and the inter-observer agreement is good. Patient age and diabetes mellitus influence the presence of EIC.
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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 | 0.012 |
| 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 | 0.001 | 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".