INNV-40. REAL WORLD INTEGRATION OF THE NEUROLOGIC ASSESSMENT IN NEURO-ONCOLOGY (NANO) SCALE IN CLINICAL PRACTICE IN PATIENTS WITH IDH-WT GBM
Bibliographic record
Abstract
Abstract BACKGROUND The neurologic assessment in neuro-oncology (NANO) scale was developed as a standardized metric to objectively measure neurologic function in patients with brain tumors and complement radiographic assessment in defining overall outcome. The scale has been incorporated in clinical trials. Early data is suggestive of feasibility and that NANO contributes to overall outcome assessment. However, real-world use of the NANO scale to drive clinical-decision making and the predictive value of the NANO scale to determine overall survival remains unclear in IDH-wt GBM. METHODS We report on an ongoing study using the NANO scale to evaluate neurologic function in patients with IDH-wt GBM, seen at Dana-Farber Cancer Institute (DFCI). Patient demographics, tumor histology and molecular status, treatment history and progression dates are being captured. NANO score, as collected by a built-in scale in our institutional electronic medical record (EMR), functional status (Karnofsky performance status) and corticosteroid dose are collected at prespecified time points (prior to start of therapy, and during each subsequent MRI visit). Changes in the NANO score will be correlated to overall survival. Statistical analyses including descriptive data analysis and generalized linear models will be performed using R (version 3.4.3). RESULTS Since June 2020, 50 patients have been enrolled in this study, including 42 (84%) with ≥2 follow up visits. Study accrual was initially impacted by the COVID-19 pandemic, but adaptation to a virtual platform for NANO allowed for improved recruitment and follow up of patients. Study results will be available for discussion at the 2021 SNO conference. CONCLUSIONS Evaluation of neurologic function by NANO is feasible in a virtual framework in a prospective study in patients with GBM, aided by integration of the scale in our institutional EMR. NANO is able to objectively track neurologic function throughout disease course in IDH-wt GBM.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".