P.097 Glioblastoma treatment, end-of-life resource utilization, and outcomes in Ontario
Bibliographic record
Abstract
Background: The end-of-life (EoL) phase of care is pivotal for glioblastoma (GBM) patients. While early integration of palliative care has shown benefits in various cancer types, its role in GBM care remains underexplored. This study aims to characterize EoL care patterns in GBM patients, assessing their temporal evolution, regional disparities, and socioeconomic influences. Methods: This is retrospective study of all patients with GBM treated in Ontario between 1994 and 2018 using the ICES data repository. Variables analyzed included patient demographics, comorbidities, palliative care utilization, and aggressive/supportive care components. Results: We identified 9,013 GBM patients within the study period. There was a gradual increase in palliative care utilization over time, accompanied by a decrease in in-hospital deaths. However, the proportion of patients receiving chemotherapy in the last 14 days of life increased. Multivariate logistic regression found socioeconomic status influenced palliative care access and rural patients also had a higher rate of in-hospital deaths, possibly due to limitations in outpatient palliative care services. Conclusions: The findings in this study clarify the status of EoL care for GBM patients within Ontario, and demonstrates key areas for future research, underscoring the need for standardized EoL care practices to enhance the quality of care for GBM 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".