Abstract W MP18: NCCT ASPECTS Underestimates Infarct Core in Early Presenters with Poor Collateral Status
Bibliographic record
Abstract
Background and Purpose: Noncontrast CT (NCCT) identifies early ischemic change that can predict irreversible ischemic injury. However DWI is more sensitive than NCCT in detection of early ischemic lesion. Our aim was to investigate the factors that influence discrepancy in early ischemic change detection between CT and DWI among good NCCT scan. Methods: We collected consecutive 167 ischemic stroke patients with occlusion of ICA and/or MCA M1 diagnosed by CTA and DWI within 6h of onset (last seen normal, LNT) between August 2004 and February 2013. Alberta Stroke Program Early Computed Tomography Score (ASPECTS) was used to evaluate discrepancy between lesions on NCCT and DWI MR. We identified 109 patients with a good NCCT scan defined as ASPECTS 6-10. Discrepancy between NCCT and DWI was when DWI ASPECTS was 0-5 and no discrepancy was when DWI ASPECTS was 6-10. Regional leptomeningeal collateral (rLMC) score by CTA was used to evaluate collateral status. Results: We reviewed 109 patients (mean age 67.5 ± 12.5 years) with median baseline National Institutes of Health Stroke Scale (NIHSS) 14 (interquartile range, 10-19). Discrepancy group (N=40, 36.7%) had shorter time from LNT to CT (median 98 vs 132 min, p=0.013), higher score of initial NIHSS (median 17 vs 13, p=0.013), lower rLMC score (median 10.5 vs 14, p<0.001). There was no significant difference from CT to DWI time (median 41 vs 40 min) between both groups. In a multivariable logistic regression analysis, time from LNT to CT (OR 0.99; 95% CI 0.99-1.00; P=0.05), rLMC score (OR 0.21; 95% CI 0.08-0.56; P=0.002) were independently associated with the discrepancy group. Conclusion: Discrepancy between CT and DWI is common in patients with acute anterior circulation ischemic stroke. This discrepancy is more apparent when patients present early and have poor collaterals.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".