P27.30.B VALIDATING THE NEUROLOGIC ASSESSMENT IN NEURO-ONCOLOGY SCALE FOR VIRTUAL VISITS: A PILOT STUDY
Bibliographic record
Abstract
Abstract BACKGROUND The Neurological Assessment in Neuro-Oncology (NANO) scale is a tool used to objectively assess nine clinically relevant domains of neurological function in patients with brain tumors to complement radiographic assessment and other clinician and patient-reported outcomes. Virtual visits for brain tumor patients are feasible and can improve clinical trial access for patients. The use of NANO in virtual care has not been validated, and we aim to determine whether a virtual NANO (vNANO) assessment is a feasible and reproducible alternative to in-person NANO for patients with glioblastoma. METHODS The study is a single-centre prospective study designed to evaluate and validate a modified NANO assessment for virtual visits. Instructions for the vNANO assessment were formulated and verified by 4 different neurologists including 3 neuro-oncologists and 1 expert in virtual neurological care. As part of the study design, a virtual visit is scheduled within one week of the routine standard of care in-person clinic visit and is done over Zoom for Healthcare. vNANO is performed independently using the vNANO scorecard by two providers sequentially and separately to assess inter-observer variability. Both evaluators are blinded to the MRI results to limit bias. An in-person visit is scheduled within one week of vNANO, and a NANO is performed by the same first provider to assess intra-observer variability. The duration of the virtual and in-person assessments are captured. Patients and clinicians are asked to complete a telehealth satisfaction survey at the end of the in-person visit. The primary endpoints are to demonstrate that 1) an adapted vNANO assessment is feasible and 2) to determine inter- and intra-observer concordance between in-person and vNANO. The secondary endpoint is to assess patient and clinician satisfaction with virtual versus in-person visits. RESULTS Sixty patients with glioblastoma will be enrolled in the study and accrual is ongoing at the time of submission (> 18 years old, English-speaking, KPS > 60). Preliminary results indicate that vNANO is time efficient and feasible with all patients completing the vNANO assessments without significant logistical or technological difficulties to date. CONCLUSION Our study will compare in-person and virtual NANO assessments in glioblastoma patients. If validated, vNANO may be adopted for virtual visits in the context of clinical trials for glioblastoma patients. The study is funded by the Academic Health Science Centre Alternative Funding Plan Innovation Fund.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".