End-of-life care in glioblastoma: A population-based study
Bibliographic record
Abstract
BACKGROUND: The end-of-life (EoL) phase of care is inevitable for glioblastoma (GBM) patients; however, it lacks standardization. This study aimed to characterize the utilization of care at EoL in GBM patients, focusing on trends over time, regional variability, and the influence of socioeconomic factors. METHODS: This was a retrospective population-based cohort study of all patients with GBM treated in Ontario between 1994 and 2018 using administrative health data and registries available at ICES (formerly the Institute for Clinical Evaluative Sciences). The proportion of patients with palliative care, supportive care, and in-hospital deaths by year of diagnosis was estimated, and trends were assessed with the Cochrane-Armitage trend test. RESULTS: The cohort included 9013 GBM patients. There was an increase in supportive care components over the study time period (29.6% in 1994-1998 to 60.2% in 2014-2018; P < .0001). There was a simultaneous decrease in in-hospital deaths (50.5% in 1994-1998 to 21.4% in 2014-2018; P < .001) and hospitalizations within 30 days before death (65.5% in 1994-1998 to 51.7% in 2014-2018, P < .001). This coincided with an increase in chemotherapy administration within 14 days of death and intensive care unit admissions within 30 days of death over the studied period of time. Patient neighborhood income level and geographic location influenced EoL care patterns with regard to both supportive and aggressive components. CONCLUSIONS: Over time there was an expansion of both inpatient and outpatient palliative care use at EoL. Rurality and neighborhood income quintile significantly influenced the utilization of these resources, underscoring the need for standardized EoL care practices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".