Abstract T P20: Correlation Between Whole Brain Ct Perfusion Maps And Non-contrast Ct Alberta Stroke Program Early Ct Score (aspects) In Patients Undergoing Acute Endovascular Stroke Interventions
Bibliographic record
Abstract
Introduction: Tools for evaluating risk of post-intervention risk hemorrhage in the setting of acute stroke include a noncontrast head CT based Alberta Stroke Program Early CT score (ASPECTS) and contrasted CT based perfusion plots. The correlation between these parameters is unknown. Methods: We performed a retrospective analysis of a prospectively collected endovascular stroke database of patients with M1 middle cerebral artery occlusion who underwent endovascular recanalization. We reviewed admission preintervention noncontrast CT for Alberta Stroke Program Early CT score (ASPECTS) and 320-detector row whole brain CT perfusion parameters (Cerebral blood volume and time to peak). Pearson correlation was determined between cerebral blood volume on the side of the stroke and ASPECTS. Results: ASPECTS and CT perfusion map were identified in 45 and 43 patients respectively. Statistically significant correlation was found between ASPECTS and cerebral blood volume data on CT perfusion MAP. (p=0.034, r=-0.28). The correlation coefficient is very weak. No correlation could be found between time to peak and ASPECTS. Conclusion: A statistically significant, but weak correlation exists between ASPECTS and CBV. Further research is needed to assess the physiological meaning of diverse imaging modalities utilized in the acute stroke setting.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".