The Montreal Cognitive Assessment (MoCA) in neuro-oncology: A pilot study of feasibility and utility in telehealth and in-person clinical assessments
Bibliographic record
Abstract
Background: Cognitive impairments are a common burden for patients with primary CNS tumors. Neuropsychological assessment batteries can be too lengthy, which limits their use as an objective measure of cognition during routine care. The purpose of this study was to evaluate the feasibility and utility of the brief Montreal Cognitive Assessment (MoCA) in routine in-person and telehealth visits (as a result of the global COVID-19 pandemic) with neuro-oncology patients. Methods: Seventy-one adults with primary CNS tumors completed MoCA testing in person (n = 47) and via telehealth (n = 24). Correlation analysis and patient-reported outcomes (PROs), including symptom burden and interference, perceived cognition, general health status, and anxiety and depression, were included in this study. Feasibility was assessed through a provider satisfaction questionnaire. Results: Patients were primarily White (83%), college-educated (71%) males (54%) with high-grade tumors (66%). The average total score on the MoCA administered in person was 25 (range: 6-30), with 34% classified as abnormal, and the average total score via telehealth was 26 (range: 12-30), with 29% classified as abnormal. Providers reported satisfaction in using the MoCA during routine clinical care, both in person and via telehealth. Lower MoCA scores correlated with worse symptom severity, KPS, age, education, and previous treatment. Conclusions: The MoCA was feasible in clinical and telehealth settings, and its relationship to clinical characteristics and PROs highlights the need for both objective and patient-reported measures of cognition to understand the overall cognitive profile of a patient with a CNS tumor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".