White matter disease and recovery following endovascular thrombectomy after acute ischemic stroke (P11-5.006)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».