Abstract W P3: Core Infarct Size Agreement Between NCCT And DWI: Implications For Patient Selection For IAT
Bibliographic record
Abstract
Background: Pre-treatment infarct volume is an important determinant of outcome after intra-arterial therapy (IAT). Ongoing IAT trials are using MRI DWI and noncontrast CT (NCCT) to quantify core infarction on the Alberta Stroke Program Early CT Score (ASPECTS) for patient selection. However, the degree to which these imaging approaches agree remains uncertain. Methods: Consecutive acute ischemic stroke patients over a 2-year period were included in this analysis if they had occlusion of the intracranial ICA or MCA M1 segment and underwent CT and MRI within 1 hour of each other and within 8 hours of onset. Two raters independently scored ASPECTS on both NCCT and DWI in separate reading sessions. Modified DWI-ASPECTS (including only >20% regional involvement) was also scored. Differences were resolved by consensus, and the consensus scores were compared across the three scales using Bland-Altman analysis. Agreement was also assessed based on a dichotomized ASPECTS threshold of 0-5 vs. 6-10 using the kappa statistic. Results: There were 74 patients with mean age 69.6 years and median NIHSS 17. Occlusions involved the ICA in 27 (36 %) patients. Mean interval between CT and MRI was 37±15 minutes. The median (IQR) ASPECTS scores were 7 (4-8) on NCCT, 5 (3-7) on DWI (P<0.0001 vs. NCCT), and 7 (4-8) on modified DWI (P=0.42 vs. NCCT). In Bland-Altman analysis, NCCT ASPECTS was mean 1.4 points (95%CI: -1.9 to 4.7 points) higher than DWI ASPECTS, and was mean 0.2 points (95%CI: -3.4 to 3.9 points) higher than modified DWI ASPECTS. For NCCT, DWI, and modified DWI, ASPECTS 6-10 scores were seen in 69%, 41%, and 60% of patients, respectively. The inter-scale agreement for ASPECTS >5 was only moderate between DWI and NCCT (kappa=0.47) and good between modified DWI and NCCT (kappa=0.68). However, differences between modified DWI and NCCT for dichotomized ASPECTS were found in 15% of patients. Conclusion: Using ASPECTS, modified DWI has better agreement with NCCT than standard DWI evaluation. However, there is a sizeable proportion of patients where dichotomized ASPECTS differs even with the use of modified DWI. This has important implications for patient selection in clinical trials.
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".