P15.13 * THE NEUROLOGIC ASSESSMENT IN NEURO-ONCOLOGY (NANO) SCALE: A TOOL TO ASSESS NEUROLOGIC FUNCTION FOR INTEGRATION IN THE RADIOLOGIC ASSESSMENT IN NEURO-ONCOLOGY (RANO) CRITERIA
Bibliographic record
Abstract
BACKGROUND: The Macdonald criteria and RANO criteria define radiologic parameters to classify therapeutic outcome among malignant glioma patients. While both scales specify that clinical status must be incorporated for overall assessment, neither provides specific parameters to do so. Furthermore, both scales prioritize clinical status over radiology in that response requires at least stable clinical status, while clinical deterioration is sufficient to declare progression. We hypothesized that a standardized metric to measure neurologic function will permit better overall response assessment in neuro-oncology. METHODS: An international group of neuro-oncologists convened bi- weekly for the past year to draft the Neurologic Assessment in Neuro-Oncology (NANO) criteria as an objective and quantifiable metric of neurologic function evaluable during a routine office examination. RESULTS: The NANO scale is a quick, clinician-friendly, and quantifiable evaluation of eight relevant neurologic domains based on direct observation/testing conducted during routine office visits. The score defines criteria for domain-specific and overall scores of response, progression, stable disease and not assessed. A given domain will be scored non-evaluable if it cannot be accurately assessed due to pre-existing conditions, co-morbid events, and/or concurrent medications. CONCLUSION: The NANO criteria aims to provide a more detailed and objective measure of neurologic function than currently exists. These criteria are designed to enable a consistent evaluation of neurologic function which will facilitate comparisons across clinical trials and therapeutic interventions. Implementation and validation of these criteria are planned, and future modifications are anticipated for further optimization.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.093 | 0.039 |
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".