INNV-30. DEVELOPMENT AND IMPLEMENTATION OF A NOVEL, BRIEF NEUROCOGNITIVE SCREENING AND REHABILITATION SERVICE IN A CLINICAL NEURO-ONCOLOGY SETTING
Bibliographic record
Abstract
Abstract BACKGROUND Although neurocognitive dysfunction is common in patients with brain tumors, existing screening methods (e.g., Montreal Cognitive Assessment) lack sensitivity for detecting neurocognitive difficulties. Moreover, existing neurocognitive screens provide only modest clinical utility for characterizing individual neurocognitive strengths/weaknesses critical for treatment planning. METHODS Beginning in February 2023, the Alvord Brain Tumor Center at the University of Washington Medical Center (UWMC) implemented the Rehabilitation Neuropsychology Screening Service (RNSS). The RNSS is an internal outpatient service staffed by a rehabilitation neuropsychologist from the Department of Rehabilitation Medicine at UWMC. Neuro-oncologists and advanced practice providers placed referrals to the RNSS based on clinical need and cognitive symptoms. On average, 1-2 referrals per week were received. RESULTS Patients referred to the RNSS have included those with high- and low-grade gliomas with varying degrees of everyday functioning status (e.g., independence at home, goals to return to work/school) and neurocognitive symptoms. The goal of the RNSS is to explore inclusion and exclusion criteria that optimize patient experience and outcomes while maximizing outreach to patients in need of neurorehabilitation services. The RNSS includes brief, repeatable neuropsychological testing (90-minutes) and patient-reported outcomes (e.g., mood, quality of life). A separate feedback appointment is then held with patients to describe personal neurocognitive strengths/weaknesses and provide neurorehabilitation strategies or rehabilitation resources/referrals based on findings. In select cases, RNSS has served as a triage stage for stepped care towards referrals to a more comprehensive neuropsychological evaluation. CONCLUSION The development and implementation of this novel approach to neurocognitive screening and rehabilitation, including the rationale, workflow of patient care, and expected outcomes, is described. With additional quality assessment/improvement, the RNSS may serve as a model for integrating neurorehabilitation services into existing neuro-oncology clinics, and may be an additional guide for patients and families towards neurorehabilitation resources and education.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".