BIOM-62. BASELINE NEUROLOGICAL FUNCTION ASSESSED BY NANO PREDICTS PROGRESSION-FREE SURVIVAL IN GLIOBLASTOMA
Bibliographic record
Abstract
Abstract BACKGROUND The Neurologic Assessment in Neuro-Oncology (NANO) scale is a standardized clinician-friendly tool consisting of nine domains to assess neurological status in brain tumor patients. Combined with MRI, NANO offers a comprehensive clinician response outcome, particularly when clinical and radiologic responses diverge. Despite its growing use in clinical trials, NANO’s association with progression-free survival (PFS) and overall survival (OS) in glioblastoma (GBM) remains underexplored. METHODS The Molecular, Imaging, and Neurological Assessment Database for CNS tumors (MIND-CNS) is a prospective, multimodal database capturing molecular, imaging, and neurological information—including NANO scores and Karnofsky Performance Status (KPS) —at baseline and follow-up visits in glioma patients. We analyzed baseline NANO scores in relation to PFS in a real-world cohort of GBM patients. Survival distributions were compared using log-rank tests, and covariate effects were estimated via Cox proportional hazards models. RESULTS We evaluated 85 GBM patients (59% male, median age 63) with baseline NANO scores as part of their assessment at a tertiary Canadian center. Patients with any neurological deficit at baseline showed a trend towards shorter PFS (p < 0.06). Specifically, deficits in gait (p < 0.001), strength (p < 0.001), and facial function (p < 0.03) were associated with shorter PFS. Greater neurological deficit in multiple domains (higher cumulative baseline NANO score) also predicted shorter PFS (p < 0.04) and higher rates of progression at 3 months (p < 0.04), independent of age, sex, and MGMT promoter methylation status. KPS score was also independently associated with PFS at 3 months (p <0.04). CONCLUSION Baseline NANO scores predict PFS and early progression in GBM patients. While KPS also correlated with outcomes, NANO offered greater granularity by identifying specific deficits. A combined international cohort analysis of GBM patients (n = 181) is underway to further evaluate associations with overall survival.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".