LONGITUDINAL NEUROLOGICAL ASSESSMENTS IN BRAIN TUMOR PATIENTS: CORRELATION BETWEEN NANO DOMAINS AND PERFORMANCE STATUS
Bibliographic record
Abstract
Abstract The Karnofsky Performance Status (KPS) and Eastern Cooperative Oncology Group (ECOG) performance status are clinician-reported outcomes critical in guiding management and prognostication in patients with primary brain tumors as they can reflect longitudinal functional changes. However, they are subjective. The Neurologic Assessment in Neuro-Oncology (NANO) scale is a metric with specific neurological domains that was subsequently developed to provide an objective standardized measure of the neurologic functional status of patients, but how longitudinal changes in NANO scale relate to KPS or ECOG scores is unknown. METHODS: A linear mixed model analysis involving 111 patients with primary brain tumors was performed. RESULTS: Statistically significant associations were identified between changes in specific NANO domains and KPS or ECOG scores. After controlling for time course both in univariable and multivariable analyses, worsening scores in gait, language and behavior domains showed significant association with decreased KPS. Gait and behavior were demonstrated to be significantly associated with ECOG both in univariable and multivariable analyses. CONCLUSIONS: The neurologic status of patients with a primary brain tumor is closely related to their performance status. In particular, the patient’s cognitive status and mobility have the greatest impact on their functional status and therefore their prognosis. Interventions and supportive care targeting these domains of function may help improve the outcomes of patients with brain tumors.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".