Abstract WP49: Performance of CT, CTA, and MRI on Decision to Treat Emergent Large Vessel Occlusion (ELVO) in Patients who Present Greater Than 6 Hours After Stroke Onset
Bibliographic record
Abstract
Background and purpose: We compared the value of Alberta Stroke Program Early Computed Tomographic scoring using CT (CT ASPECTS), collateral score on CT angiography (CTA), ASPECTS using diffusion-weighted MRI (DWI ASPECTS), DWI lesion volume, and DWI volume with National Institute of Health Stroke Scale (NIHSS) in determining candidacy of patients who presented >6 hours from stroke onset. Methods: Decision to treat was first determined for each test alone and then with knowledge from other tests. A dismantling design was used to determine the additive effects of each test. Any discrepancy between the first and subsequent decisions to treat, in terms of sensitivity and specificity, is the impact of gained knowledge and was assessed using a generalized mixed-model assuming a binary distribution with PROC GLIMMIX/SAS. Inter-rater reliability was examined using weighted-Kappa. Results: We identified 39 patients between December 1st, 2015 and June 30th 2016. Median time from last-known normal to non-contrast CT was 492 minutes. Median interval between non-contrast CT and CTA was 7 minutes, and between CTA and MRI, 75.5 minutes. For sensitivity, effect of knowledge gained from successive tests was not significant (Table 1; Fig. 1). However, significant gains in specificity were observed from successive tests (63% to 84%; p<.01). In particular, specificity increased by 14% (p=.09), 18% (p=.02), and 12% (p=.07), for DWI ASPECTS, DWI Volume, and DWI Volume+NIHSS, respectively. Inter-rater reliability was between .34-1.0 for each test. Conclusion: CT, CTA and MRI each have the ability to correctly determine ELVO patients who would be candidates for embolectomy. However, identification of poor candidates for endovascular therapy was significantly improved using diffusion-weighted 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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".