White matter disease and recovery following endovascular thrombectomy after acute ischemic stroke (P11-5.006)
Bibliographic record
Abstract
Objective: To determine the impact of chronic white matter lesions (WML) on functional outcomes in patients with acute stroke who underwent endovascular thrombectomy (EVT). Background: The evidence regarding the impact of chronic WML on functional outcomes after EVT is mixed. Design/Methods: A prospective stroke center registry (10/2019–06/2021) of consecutive adult patients with acute stroke was queried for patients with ICA or M1 occlusions who had undergone magnetic resonance imaging (MRI). Multivariable logistic regression was used to estimate the relationship between age, Alberta Stroke Program Early Computed Tomography Scale score, National Institutes of Health Stroke Scale, occlusion location, and successful recanalization (thrombolysis in cerebral infarction score of 2b–3 versus 0–2a or no thrombectomy) on good functional outcome (90-day mRS 0–2). Mediation analysis was used to estimate the effect of WML severity on age as a predictor of good functional outcome following successful recanalization. Results: Among the 121 included patients, the median age was 67y (IQR 58–77), 49 (40.5%) were female, and 39 (32.2%) had a Fazekas score of 2 or 3. In unadjusted regression, age was associated with WML severity (step 1: OR 1.03, 95% CI 1.02–1.04, p<0.001), and age was associated with an unfavorable 90-day mRS (step 2: proportional OR 0.98, 95% CI 0.95–0.99, p = 0.027). WML severity was also associated with 90-day mRS (step 3: OR 0.74, 95% CI 0.52–1.06, p=0.096). In multivariable regression, the total effect of age on unfavorable shift in 90-day mRS remained significant (OR 0.96, 95% CI 0.94–0.98, p<0.001), with a trend toward a persistent indirect mediator effect of WML (p=0.084). The mediator WML severity explained 23% of the association of age with 90-day mRS. Conclusions: In this single center analysis, WML burden partially mediated the effect of age on functional recovery in acute stroke. Disclosure: Mr. Vigilante has nothing to disclose. Ms. Koneru has nothing to disclose. Ms. Penckofer has nothing to disclose. Mr. Sprankle has nothing to disclose. Dr. Patel has nothing to disclose. Dr. Khalife has nothing to disclose. Dr. Oliveira has nothing to disclose. Scott Kamen has nothing to disclose. An immediate family member of Dr. Thon has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Horizon. An immediate family member of Dr. Thon has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Genentech. An immediate family member of Dr. Thon has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Genentech. Dr. Thon has received personal compensation in the range of $500-$4,999 for serving as a Focus group participant with Alexion. Dr. Siegler has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Ceribell. Dr. Siegler has received personal compensation in the range of $10,000-$49,999 for serving on a Speakers Bureau for AstraZeneca.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".