Abstract B042: A survey of Neuro-Oncology (NOB) clinical providers to assess the feasibility and utility of the administration of the Montreal Cognitive Assessment (MoCA) cognition duo app in an outpatient clinic
Bibliographic record
Abstract
Abstract Introduction: Primary brain tumor (PBT) patients experience significant cognitive decline, and early symptom detection helps clinical providers identify and manage impairments as part of routine care. The Montreal Cognitive Assessment (MoCA) is a brief screening tool consisting of standardized assessments of eight key cognitive domains. Our group previously reported the feasibility of use in telehealth and clinical care, and here we explore the feasibility and utility of a newly developed App (MoCA Duo APP) for routine patient care through provider and assessor feedback. Methods: The MoCA assessment was used for 178 examinations using the MoCA Cognition Duo APP by trained MoCA assessors, with results shared with NOB healthcare providers prior to patient visits. After five months, ten providers (Physicians = 5, Nurse Practitioners = 5) were sent a 12-item survey through SurveyMonkey that assessed the general use of the MoCA Duo APP in patient care with the utility of use reported here. Results: All ten providers completed the survey, with half reporting using the MoCA before working in the NOB clinic. Most providers (n=9) reported discussing the patient's MoCA results with their clinical teams and with patients before/after clinic visits. Seven providers endorsed being able to use the MoCA duo APP results to accurately assess cognition at all visits, with three indicating patient-specific limitations, including patient anxiety about testing, deficits impacting test completion and results (i.e., hearing, or motor deficits), and language barriers for those with English as a second language affecting utility in some cases. When asked for limitations of use over their usual exam, 3 noted using the MoCA Duo APP assessment aligned with their current exam/clinical impression, 3 reported the MoCA assessment identified a previous deficit they were aware, and 2 noted the discovery of new deficits with the MoCA Duo APP use. Qualitative descriptions included describing (‘the MoCA as the best screening tool’ and ‘deficits found on the MoCA were not found on the exam’) showed its use as a framework for further diagnostic investigations. Qualitative comments also identified relevance to clinical care and endorsement of the importance of cognitive assessment by patients and caregivers. Conclusions: Our findings demonstrate the utility of the MoCA Duo APP in PBT patients by a diverse group of healthcare providers and assessors. Implementing the MoCA Duo APP allowed providers to evaluate patients' cognitive status and confirm known deficits promptly. As reported elsewhere, the median time for completion was 10 minutes, and automatic scoring reduced post-assessment time, allowing for immediate provider use. Further research is needed to evaluate environmental impacts and deficit adaptation, use in determining interventions, and impact on health outcomes. Citation Format: Madhura V. Managoli, Jaime Garcia, Morgan Johnson, McKenzie C. Kauss, Yeonju Kim, Hope Miller, Maeve Pascoe, Elizabeth Vera, Alex R. Wollet, Vivian A. Guedes, Mark R. Gilbert, Terri S. Armstrong, Alvina Acquaye-Mallory. A survey of Neuro-Oncology (NOB) clinical providers to assess the feasibility and utility of the administration of the Montreal Cognitive Assessment (MoCA) cognition duo app in an outpatient clinic [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 B042.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".