Abstract A030: The Montreal Cognitive Assessment (MoCA) administered virtually or via Duo APP in primary brain tumor patients: a preliminary analysis
Notice bibliographique
Résumé
Abstract Background: Primary brain tumor (PBT) patients experience cognitive dysfunction (CD) because of both their tumor and its treatment. Evaluating CD in PBT populations may inform patient outcomes and clinical management and allow exploration of biologic underpinnings associated with its occurrence. The Montreal Cognitive Assessment (MoCA) is a brief assessment of objective measures of cognition that evaluates 8 specific cognitive domains. Our group has previously reported the feasibility of MoCA, and this study includes using the newly developed MoCA Duo APP to evaluate and report the cognitive function of patients with PBT. Associations between total MoCA scores and clinical and demographic characteristics were explored. Methods: This cohort consisted of adult PBT patients (n=172) enrolled in the NCI-NOB Natural History Study (NCT02851706: PI T. Armstrong). Assessments were performed between February 2020 and July 2023 by MoCA certified assessors and administered in the clinic or via telehealth. MoCA scores range from 0 to 30 (normal cognition ≥ 26). Healthcare providers collected demographic and clinical characteristics, with Karnofsky performance status (KPS) scores categorized as poor (≤ 80) or good (≥ 90). Descriptive statistics, independent t-tests, one-way ANOVAs, and Pearson’s correlation were conducted using IBM SPSS Statistics software. Results: The majority of patients were male (58%), white (81%), had ≥12 years of education (87%), had high-grade (3/4) tumors (65%), and a good KPS score (68%). The mean MoCA score was 25 (median: 26; range: 6-30) with a mean completion time of 10.7 minutes (median: 10; range: 6.1-41.1). Forty-two percent (n=73) were classified as abnormal. The median scores were lowest in the visuospatial/executive (4/5; range: 0-5), abstraction (2/3; range: 0-3) and delayed recall (4/5; range: 0-5) domains. A univariate analysis identified lower MoCA scores in patients with high-grade tumors (p<0.001), poor KPS scores (p<0.001), who underwent two or more surgeries (p=0.004) or treatments (p=0.038), had recurrence (p=0.002), had progression on current imaging (p=0.017), and had current anticonvulsant (p=0.014) and corticosteroid use (p=0.036). Age at diagnosis (r=-0.157, p=0.021) and at visit (r=-0.193, p=0.005) had a weak negative correlation with MoCA scores. Conclusions: Our results indicate that patient age, tumor progression, treatment, and concomitant medications are associated with MoCA scores. Further analysis will evaluate tumor methylation status and blood-based biomarkers associated with occurrence to develop prediction models that can be evaluated in larger cohorts. Future investigations should compare MoCA scores to other forms of cognitive assessment, including patient-reported outcomes, incorporating multiple timepoints to monitor CD in patients throughout their treatment and survivorship care. Citation Format: McKenzie C. Kauss, Elizabeth Vera, Kimberly Reinhart, Hope Miller, Jaime Garcia, Morgan Johnson, Madhura V. Managoli, Maeve Pascoe, Kaitlynn Slattery, Alex R. Wollet, Mark R. Gilbert, Alvina Acquaye-Mallory, Terri S. Armstrong, Vivian A. Guedes. The Montreal Cognitive Assessment (MoCA) administered virtually or via Duo APP in primary brain tumor patients: a preliminary analysis [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr A030.
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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».