NCOG-43. NEUROCOGNITIVE IMPAIRMENT AND FRAILTY IN GERIATRIC PATIENTS WITH HIGH GRADE GLIOMA AND THORACIC MALIGNANCY
Bibliographic record
Abstract
Abstract BACKGROUND The median age at diagnosis for high grade glioma is 64 years. With peak incidence 75-84, malignant glial tumors are frequently a disease of the elderly. Common assessment measures fail to accurately gauge geriatric cancer patient fitness. Comprehensive Geriatric Assessment (CGA) is recommended in patients older than 65 to gauge risk of toxicity and tolerance of therapeutic intervention. We reviewed data for older patients with high grade glioma (HGG) and thoracic malignancy (TM) who underwent CGA via Senior Oncology Clinic (SOC) at Levine Cancer Institute. METHODS From 2015 to 2019 104 thoracic malignancy patients and 19 high grade glioma patients completed CGA via SOC before treatment or a required change in therapy. Data was incorporated into the LCI Senior Oncology Database by the REDCap secure web application, allowing for both quantitative and qualitative data analysis. RESULTS The median age was 77 in the HGG cohort compared to 80 years with TM. The physician rated Karnofsky Performance Status (KPS) for HGG and TM were similar (76% v 79%) as were the percentages of patients that were frail or prefrail (90% v 87%). Montreal Cognitive Assessment scores were lower in HGG (20 v 23). Considerably more HGG had falls in the 6 months before their assessment (58% v 30%) and gait speed was slower (0.76 m/s v 0.85 m/s). CONCLUSIONS Older patients with high grade gliomas compared to similar thoracic malignancies had more neurocognitive impairment, falls in the preceding 6 months, and slower gait speed. Physician rated KPS and frailty were similar in both groups. The results illustrate the limitations of physician-rated performance measures and highlight the importance of CGA in older 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".