Geriatric assessment of glioblastoma patients is feasible and may provide useful prognostic information
Bibliographic record
Abstract
BACKGROUND: Glioblastoma (GBM) is the most common and most lethal primary brain tumor in adults. Clinical trials in older patients with GBM have explored the use of single and multimodality treatment regimens with modest survival benefits; however, trial criteria are commonly based on chronological age and do not reflect the heterogeneity of this cohort. Geriatric assessment (GA) techniques predict survival and treatment tolerance in other tumor sites and thus may objectively guide the decision-making process, but data are lacking in the neuro-oncology cohort. METHODS: We performed a prospective, multicenter feasibility study involving patients age 65 years or older with newly diagnosed GBM. A modified GA was undertaken in the outpatient setting prior to starting treatment. Feasibility was determined primarily by recruitment rate, alongside data completeness, impact on clinic time, and acceptability to patients and staff. Factors associated with survival were explored using Cox regression models. RESULTS: Fifty patients were recruited within a prespecified time period with a recruitment rate of 82% (target 80%). Data completeness was greater than 80% in all except one assessment. Median overall survival was 9.5 months (95% confidence interval [CI] 5.0-14.0 months). Among the GA screening factors analyzed, a baseline impaired Montreal Cognitive Assessment (hazard ratio [HR] = 2.7, 95% CI 1.128-6.530) and impairment in instrumental activities of daily living (HR = 2.9 95% CI 0.983-8.541) were associated with poorer survival. CONCLUSION: In the first study of this kind among elderly GBM patients, we have shown that undertaking a neurologically focused GA screen is feasible and may provide useful prognostic information.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".