The Neurologic Assessment in Neuro-Oncology (NANO) scale: a tool to assess neurologic function for integration into the Response Assessment in Neuro-Oncology (RANO) criteria
Bibliographic record
Abstract
Background: The Macdonald criteria and the Response Assessment in Neuro-Oncology (RANO) criteria define radiologic parameters to classify therapeutic outcome among patients with malignant glioma and specify that clinical status must be incorporated and prioritized for overall assessment. But neither provides specific parameters to do so. We hypothesized that a standardized metric to measure neurologic function will permit more effective overall response assessment in neuro-oncology. Methods: An international group of physicians including neurologists, medical oncologists, radiation oncologists, and neurosurgeons with expertise in neuro-oncology drafted the Neurologic Assessment in Neuro-Oncology (NANO) scale as an objective and quantifiable metric of neurologic function evaluable during a routine office examination. The scale was subsequently tested in a multicenter study to determine its overall reliability, inter-observer variability, and feasibility. Results: The NANO scale is a quantifiable evaluation of 9 relevant neurologic domains based on direct observation and testing conducted during routine office visits. The score defines overall response criteria. A prospective, multinational study noted a >90% inter-observer agreement rate with kappa statistic ranging from 0.35 to 0.83 (fair to almost perfect agreement), and a median assessment time of 4 minutes (interquartile range, 3-5). Conclusion: The NANO scale provides an objective clinician-reported outcome of neurologic function with high inter-observer agreement. It is designed to combine with radiographic assessment to provide an overall assessment of outcome for neuro-oncology patients in clinical trials and in daily practice. Furthermore, it complements existing patient-reported outcomes and cognition testing to combine for a global clinical outcome assessment of well-being among brain tumor patients.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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".