Abstract 2840: Aspect Score: DWI or CT Scan, Which One Is Better Marker For Stroke Morbidity And Outcome?
Bibliographic record
Abstract
Background and Objective The Alberta Stroke Program Early CT Score (ASPECTS) is a validated grading system to assess ischemic changes on CT in acute ischemic stroke. Magnetic resonance imaging with diffusion weighted imaging (DWI) sequence is commonly used to identify the final ischemic changes. We examined the difference between the relationship of NIHSS at admission and ASPECT score calculated using CT scan versus MRI DWI sequence. Methods We conducted a retrospective analysis of prospectively collected data from 99 cases of acute ischemic stroke treated with IV rt-PA by time criteria, admitted to Mayo Clinic from March, 2002 through June, 2011. CT head at 24 hours and MRI DWI sequence were used to assign ASPECT score. We dichotomized ASPECTS (categorized as 0 to 7 versus 8 to 10) and favorable patient outcome at 3 month (modified Rankin score less than equal to 2 and more than 2). Univariate analysis including t-test, Chi-square, and Fisher Exact test was used when appropriate. Results Mean age was 70±14 years. Mean admission NIHSS score was 8±4. DWI ASPECTS (p<0.001) and CT ASPECTS (p=0.127) were inversely associated with admission NIHSS. Higher (8-10) CT ASPECTS (p=0.001) or DWI ASPECTS (p=0.002) were associated with good outcome (mRS ≤2) at 3 months. Sensitivity, specificity, positive predictive value and negative predictive value for good outcome identified by CT ASPECTS versus DWI ASPECTS were 81% vs 52%, 54% vs 54%, 83% vs 59% and 50% vs 47% respectively. Conclusion CT and MRI DWI are comparably useful to calculate the ASPECTS for estimation of functional outcome, but CT scan at 24 hours may be more sensitive for the prediction of good recovery.
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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) | 0.001 | 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) | 0.008 | 0.002 |
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".