Abstract P485: Predictors and Clinical Impact of Deep Grey Matter Infarction After Endovascular Treatment for Large Vessel Occlusion Stroke: Results From the Escape-NA1 Trial
Bibliographic record
Abstract
Purpose: In this post-hoc analysis of the ESCAPE-NA1 trial, we investigated the prevalence of deep grey matter infarcts and their influence on clinical outcome. Methods: Infarcts on 24 hour follow up imaging (non contrast head CT or diffusion-weighted MRI) were categorized as predominantly deep grey matter infarcts (caudate and/or lentiform nucleus infarcts with sparing of the superficial grey matter and white matter) vs. other infarcts. Total infarct volume was manually segmented in all patients. When MRI follow-up was available, deep grey matter and grey matter infarct volumes were segmented separately. Multivariable logistic regression with adjustment for key minimization variables and by infarct volume was used to assess the association of predominantly deep grey matter infarcts and good outcome. Results: Of the 1026 included patients, 316 (30.8%) had predominantly deep grey matter infarcts. Cumulative proportions of good outcome for overall, grey matter, deep grey matter, and superficial grey matter infarct volumes are shown in the figure. Good outcomes were more frequently achieved in patients with predominantly deep grey matter infarcts (239/316 [75.6%] vs. 374/704 [53.1%]). Deep infarcts were tightly correlated with infarct volume (Pearson rho -0.35) and in multivariable analysis deep grey matter infarcts were predictive of outcome overall; when examined in volume percentiles, there was no effect of deep infarct location. Conclusion: Predominantly deep grey matter infarcts are associated with good outcomes. Deep grey matter infarct location favorable prognosis is associated with small overall infarct size.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".