Presence of Early Ischemic Changes on Computed Tomography Depends on Severity and the Duration of Hypoperfusion
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To evaluate the clinical significance of early ischemic change (EIC) on computed tomography (CT), pertinent factors that contribute to the appearance of EIC. METHODS: Both CT and technetium-99m hexamethylpropylene amine oxime were performed on a total of 53 patients (34 men, 19 women, mean 69.7 years old) with acute embolic stroke within 6 hours of onset. Patients were excluded if they showed definite clinical recovery or were administered thrombolytic agents. EIC was evaluated using Alberta Stroke Program Early CT Score (ASPECTS). Residual cerebral blood flow (CBF) was determined on SPECT. Variables that were considered pertinent were patients' age, gender, neurological severity, symptom duration, and residual CBF. Using significant pertinent factors for EIC, separate analyses of brain swelling without hypoattenuation and parenchymal hypoattenuation were performed. RESULTS: Patients with EIC (n=37) showed severe neurological deficits, a longer duration and severe hypoperfusion. A positive correlation was observed between ASPECTS and residual CBF (P=0.002; Kruskal-Wallis test). A logistic-regression analysis revealed that both symptom duration (r=0.024, P=0.006) and severity of hypoperfusion (r= -12.167, P=0.006) are independent factors related to EIC. Symptom duration and residual CBF were significantly different among patients with parenchymal hypoattenuation (n=32), brain swelling without hypoattenuation (n=5), and no EIC (P=0.018 and P=0.001, respectively; one-way ANOVA). CONCLUSIONS: The presence of EIC is determined by the duration and the degree of hypoperfusion. This finding supports the hypothesis that tissue damage may be evaluated by a combination of onset time and 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".