NCOG-44. FEASIBILITY AND UTILITY OF THE MONTREAL COGNITIVE ASSESSMENT IN ROUTINE CLINICAL EXAMS AND TELEHEALTH VISITS IN NEURO-ONCOLOGY
Bibliographic record
Abstract
Abstract Cognitive dysfunction (CD) is common among primary brain tumor (PBT) patients and adds to the overall symptom burden. Standardized assessments able to be incorporated into routine clinical in-person and telehealth care are needed. Here, we report the feasibility, utility, and satisfaction with use of the Montreal Cognitive Assessment (MoCA) in telehealth and clinical settings by trained clinical providers. Feasibility and provider satisfaction were assessed through survey responses, and patient performance on the MoCA, after a reliability check, was reported through descriptive statistics. Seventy-nine MoCAs on 71 patients were completed in clinic (n=55) or telehealth (n=24). Majority of patients were white (83%) males (54%) with high grade PBTs (66%), and half of patients had completed at least a college education. In clinic, providers (n=9) reported the MoCA took 5-20 minutes to complete, was easy to incorporate into routine practice (78%), believed it was accurate in assessing cognition (67%), and was useful in determining treatment (88%). The average in-person MoCA score was 25 (range: 6 to 30), with 31% of scores classified as abnormal (≤26). In telehealth, providers (n=11) found the administration of the MoCA prior to attending participation in the telehealth visit helpful (75%), discussed the results with their clinical team (75%) and patient (63%), and believed the MoCA was accurate in assessing cognition remotely (63%). On average, patients took 13 minutes (9-22) to complete testing, with three tests discordant on reliability scoring and one patient unable to complete testing. The average telehealth MoCA score was 26 (12-30), with 29% of scores classified as abnormal. Overall, testing was feasible in both clinical and telehealth settings, and providers reported satisfaction with its use. Future studies should evaluate validity in a larger sample and include analysis of relevant cut-off scores, impact of disease, tumor treatment, and genomic predispositions.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".