NCOG-14. Deficits in Gait Quantified by Large Language Model-derived NANO Scores Predict Survival in High-grade Glioma
Bibliographic record
Abstract
Abstract BACKGROUND In neuro-oncology, imaging findings and a patient’s clinical status do not always correlate. The Neurologic Assessment in Neuro-Oncology (NANO) scale is a clinician-reported outcome assessment tool consisting of nine domains to measure neurologic function routinely assessed by clinicians. METHODS Using GPT4DFCI, Dana-Farber Cancer Institute’s (DFCI) HIPAA-secure endpoint to GPT-4o, we ran free text physical exam documentation through a secure Application Programming Interface to assess whether a large language model (LLM) can accurately generate NANO scores. We used an iterative process for prompt development. Temperature, a parameter that controls the randomness of LLM output, was set to zero. Cox proportional hazards analysis was used to predict whether NANO score at the time of bevacizumab initiation (last line therapy) could predict overall survival (OS). RESULTS We evaluated 287 patients with progressive grade 4 gliomas seen at least twice at DFCI between 1/1/2018 to 12/31/2023 from the time of bevacizumab initiation. LLM-generated scores were grouped into 0 or 1 (none to mild) versus 2 or 3 (moderate to severe) for each NANO domain. Accuracy between clinician-reported and LLM-extracted NANO scores was high (mean = 92.5%, range 76.6-100%, n=56). Average patient age at bevacizumab initiation was 61 years, 36% were female, 11% were IDH-mutant, and 41% had MGMT-unmethylated tumors. Median total NANO at initiation of bevacizumab was 1 (range 0-11). Cox proportional hazards analysis revealed that deficits in gait at bevacizumab initiation predicting worse OS (hazard ratio 2.86, 95% CI: 1.21, 6.74; p=0.02), independent of age, MGMT-status, sex, and IDH-status. CONCLUSIONS LLMs can generate NANO scores from clinical notes. Moderate to severe gait impairment by NANO was predictive of worse OS at bevacizumab initiation. LLMs offer the ability to scale NANO to understand how NANO may affect OS at different disease points for different disease groups.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.001 |
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".