A Detailed Analysis of Infarct Patterns and Volumes at 24-hour Noncontrast CT and Diffusion-weighted MRI in Acute Ischemic Stroke Due to Large Vessel Occlusion: Results from the ESCAPE-NA1 Trial
Bibliographic record
Abstract
Background The effect of infarct pattern on functional outcome in acute ischemic stroke is incompletely understood. Purpose To investigate the association of qualitative and quantitative infarct variables at 24-hour follow-up noncontrast CT and diffusion-weighted MRI with 90-day clinical outcome. Materials and Methods The Safety and Efficacy of Nerinetide in Subjects Undergoing Endovascular Thrombectomy for Stroke, or ESCAPE-NA1, randomized controlled trial enrolled patients with large-vessel-occlusion stroke undergoing mechanical thrombectomy from March 1, 2017, to August 12, 2019. In this post hoc analysis of the trial, qualitative infarct variables (predominantly gray [vs gray and white] matter involvement, corticospinal tract involvement, infarct structure [scattered vs territorial]) and total infarct volume were assessed at 24-hour follow-up noncontrast CT or diffusion-weighted MRI. White and gray matter infarct volumes were assessed in patients by using follow-up diffusion-weighted MRI. Infarct variables were compared between patients with and those without good outcome, defined as a modified Rankin Scale score of 0–2 at 90 days. The association of infarct variables with good outcome was determined with use of multivariable logistic regression. Separate regression models were used to report effect size estimates with adjustment for total infarct volume. Results Qualitative infarct variables were assessed in 1026 patients (mean age ± standard deviation, 69 years ± 13; 522 men) and quantitative infarct variables were assessed in a subgroup of 358 of 1026 patients (mean age, 67 years ± 13; 190 women). Patients with gray and white matter involvement (odds ratio [OR] after multivariable adjustment, 0.19; 95% CI: 0.14, 0.25; P < .001), corticospinal tract involvement (OR after multivariable adjustment, 0.06; 95% CI: 0.04, 0.10; P < .001), and territorial infarcts (OR after multivariable adjustment, 0.22; 95% CI: 0.14, 0.32; P < .001) were less likely to achieve good outcome, independent of total infarct volume. Conclusion Infarct confinement to the gray matter, corticospinal tract sparing, and scattered infarct structure at 24-hour noncontrast CT and diffusion-weighted MRI were highly predictive of good 90-day clinical outcome, independent of total infarct volume. Clinical trial registration no. NCT02930018 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Mossa-Basha in this issue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".