Relative, Conditional, and Overall Survival and Causes of Death in Patients With Glioblastoma: A Retrospective Longitudinal Cohort Study
Bibliographic record
Abstract
Background: Our goal in this manuscript was to perform a survival analysis and understand the causes of death (CODs) in patients with glioblastoma using the Surveillance, Epidemiology, and End Results (SEER) database. Methods: A retrospective cohort study was conducted using version 8.3.9.2 of SEER*Stat software to extract data from the SEER 17 Plus database. Patients with World Health Organization (WHO) grade IV glioblastoma diagnosed between 2000 and 2019 were included to calculate overall survival (OS), relative survival (RS), and conditional survival. R software was used to calculate univariate and multivariate Cox regression models for age, sex, and race to identify their effect on survival. Results: We included 45,071 patients with grade IV glioblastoma according to WHO 2016 classification. The observed 1-year, 3-year, and 5-year survival rates showed a decline to 40.1%, 9.8%, and 5.2%, respectively. Similarly, the relative 1-year, 3-year, and 5-year survival rates were 40.7%, 10.2%, and 5.4%, respectively. The conditional 3-year survival rates improved up to 16.9%, 42.9%, and 60.2% after 1, 3, and 5 years of survival, correspondingly, with females showing better estimates. The most common cancer CODs were the brain and other central nervous system (CNS) cancers. Among non-glioblastoma cancer CODs, breast cancer was the most common cause. Additionally, cardiovascular diseases, cerebrovascular diseases, and septicemia were the most common non-cancer CODs. Conclusion: In this study, patients with glioblastoma showed a sharp decline in OS and RS over time after diagnosis. However, there was a notable improvement in conditional 3-year survival over time. Cardiovascular diseases emerged as the most common non-cancer COD, with lower survival rates in males and advanced age.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".