OTHR-08. Pediatric Neurologic Assessment in Neuro-oncology (pNANO) Scale: A tool to assess neurologic function for Response Assessment in Neuro-oncology (RAPNO)
Bibliographic record
Abstract
Abstract Background: The Neurologic Assessment in Neuro-Oncology (NANO) scale, a standardized metric to objectively measure neurologic function in adult brain tumor patients, complements radiographic assessment in evaluating outcomes of neuro-oncology patients in clinical trials and clinical practice. Currently, there is no standardized measure for neurologic function in pediatric neuro-oncology patients despite their distinct clinical presentations and tumor locations. Therefore, we developed a dedicated pediatric NANO (pNANO) scale. Methods: An international group of pediatric neurologists and adult and pediatric neuro-oncologists convened bi-weekly over 5 months to draft the pNANO scale as an objective and quantifiable measure of neurologic function in children that can be administered during routine examination by pediatric-trained providers of any sub-specialty and be utilized together with other indicators to assess response in clinical trials. Results: Ten relevant domains of neurologic function were identified based on common pediatric brain tumor locations: gait, strength, cerebellar function, visual fields, visual acuity, facial strength, level of consciousness, extraocular movements, dysarthria and dysphagia. For each domain, developmental age appropriate levels of function were defined and categorized. Each domain, based on direct observation and testing during any routine neurological exam, can be used for longitudinal monitoring. Conclusions: The pNANO scale has been developed and aims to provide an objective metric of neurologic function for pediatric brain tumor patients. This scale will be tested for reliability, feasibility and inter-observer variability. Consistent evaluation of neurologic function using pNANO along with radiographic assessment will enable more comprehensive and standardized response assessment in pediatric neuro-oncology patients enrolled in clinical trials.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.006 | 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".