Abstract TP46: ASPECTS Performance in a Series of Patients Undergoing CT and MRI: Reader Agreement, Modality Agreement, and Outcome Prediction
Bibliographic record
Abstract
Objective: To compare the performance of pre-treatment Alberta Stroke Program Early CT scoring (ASPECTS) using NCCT and MRI in a large endovascular therapy cohort. Methods: This is a DEFUSE 2 substudy. Prospectively enrolled patients underwent baseline CT, MRI and started endovascular therapy within 12 hours of stroke onset. Inclusion criteria for this analysis were evaluable pre-treatment NCCT, diffusion-weighted MRI (DWI) and 90-day modified Rankin Scale (mRS) score. Two expert readers graded ischemic change on NCCT and DWI using the ASPECTS and were blinded to all clinical information except stroke side. ASPECTS scores were analyzed either full scale, trichotomized (0-4 vs. 5-7 vs. 8-10), or dichotomized (0-7 vs. 8-10). Good functional outcome was defined as a 90 day mRS of 0-2. Infarct volumes were calculated using rapid processing of perfusion and diffusion (RAPID) software. Results: 71 patients fulfilled our study criteria. Their mean age was 68±15 years, median NIHSS score was 17 (IQR 12-20), and 49% were female. There were 31 (44%) right-sided strokes. Reader and modality agreement are presented in the Table. Median (IQR) time between NCCT and MRI was 1.62 (1.14-2.38) hours. There was greater correlation of DWI ASPECTS with DWI volume (p<0.001) and functional outcome (p=0.001) than NCCT ASPECTS. DWI ASPECTS correlated with 90 day mRS with OR (95%) of: 12.3 (1.4-105) 8-10 vs.0-4; 4.0 (1.2-13.2) 8-10 vs. 5-7; 3.1 (0.3-30) 5-7 vs. 0-4; and 5.4 (1.8-16.2) 8-10 vs.0-7. NCCT ASPECTS did not correlate with mRS with OR (95%) of: 1.4(0.2-8.3) 8-10 vs. 0-4, 1.02(0.4-2.8) 8-10 vs. 5-7, 1.4(0.2-8.5) 5-7 vs. 0-4, and 1.1(0.4-2.9) 8-10 vs. 0-7. Conclusion: Inter-rater agreement on NCCT ASPECTS, as well agreement between NCCT and DWI ASPECTS, ranged from slight to moderate. DWI ASPECTS outperformed NCCT ASPECTS in correlation with DWI volume and predicting functional outcome at 90 days. These results may be influenced by differences in acquisition times for NCCT and MRI.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".