Abstract A030: The Montreal Cognitive Assessment (MoCA) administered virtually or via Duo APP in primary brain tumor patients: a preliminary analysis
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".