INNV-11. GLIOBLASTOMA IN THE OLDER PERSON – HOW DO WE DECIDE ON TREATMENT?
Bibliographic record
Abstract
Abstract The incidence of glioblastoma (GBM) peaks in the 7th and 8th decades of life. Multiple treatment options exist for older patients with GBM however, the assessment of older patients prior to treatment decisions is poorly researched and lacks standardization. In order to address this issue we performed a cross-sectional electronic survey distributed to all full members of the Society for Neuro-Oncology. There were 116 respondents from a total of 1515 recipients (8% response rate). The survey was distributed during the peak of COVID-19 which undoubtedly affected response rates. 97% of respondents were clinicians with 86% academic. 72% had been in practice > 10 years and the majority saw 5–10 new GBM cases per month. 95% of respondents were from the USA, with involvement from Japan, Australia, Canada and Italy. 37% of respondents routinely perform a cognitive or frailty screening test. Of these, MMSE and MoCA were the most commonly used. Of those who performed a screening test, the majority reported that the results changed their treatment decision in approximately 50% of cases. 50% of respondents have access to a multidisciplinary team during their clinic, with physical therapy being the most available. When making treatment decisions, participants ranked performance status as the most important clinical factor. Considering the heterogeneity of this patient population, we argue that performance status is a crude measure of vulnerability within this cohort. In the first survey of this kind, we have shown a disparity in assessment techniques across the international neuro-oncology field and the impact performing a cognitive screen has on decision making. Older patients with GBM represent a unique clinical scenario because of the complexity of distinguishing neuro- oncology related symptoms from general frailty. There is a need for specific geriatric assessment models tailored to the older neuro-oncology population in order to facilitate treatment decisions.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".