Abstract WP53: Can Attenuation Characteristics On Non Contrast CT Be Used In Place Of Diffusion FLAIR Mismatch In The Imaging Triage Of Acute Ischemic Stroke Patients?
Bibliographic record
Abstract
Purpose: We aimed to determine if attenuation characteristics on NCCT can be used in place of DWI FLAIR“mismatch”as an imaging paradigmto detect patients within a recommended time window for thrombolysis. Methods: Data is from consecutive acute stroke patients (2005-2009) from Keimyung University, South Korea analyzed at the University of Calgary. Only patients with visible anterior circulation occlusions on baseline CT-angio, known stroke symptom onset time and MRI within 60 minutes of baseline CT were included. All patients received revascularization therapy (IV tPA and/or IA). DWI FLAIR mismatch and CT changes at baseline were diagnosed by consensus. Ratio of ipsilateral vs. contralateral CT HU (rCT) within baseline DWI lesion was calculated. CT attenuation within DWI lesion was qualitatively graded into a) equal or more (subtle)or b) less than contralateral white matter (obvious). ROC analyses was used to compare the ability of models using DWI FLAIR mismatch vs. rCT in predicting onset to imaging time< 4.5 hrs and ICH at 24 hours. Results: Of 136 patients included [mean age 67.6 yrs (SD 11.2 yrs), 55.1% male, median onset to MR time was 159.5 minutes (IQR 128-226 mins)], 131/136 (96.3%) had DWI changes on baseline MRI. DWI FLAIR mismatch was seen in 88/136 (64.7%) andNCCT hypo-attenuation in 93/136 (68.9%). A rCT>=0.85predicted DWI FLAIR mismatch with a sensitivity of 72.5%, specificity of 75.6% and PPV of 85.3%. No patient without baseline CT changes had rCT<0.9. DWI FLAIR mismatch was not better than rCT>=0.85 in predicting onset to MRtime<4.5 hrs (AUC 0.66 95% CI 0.57-0.75 vs. 0.61 95% CI 0.51-0.70, p=0.23). Subtle or no CT hypo-attenuation within DWI lesion measured qualitatively predicted presence of DWI FLAIR mismatch. (p=0.002) Conclusion: CT hypo-attenuation is as good as DWI FLAIR mismatch in identifying patients who are good candidates for thrombolytic therapy.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".