Social determinants of health and survival on Brazilian patients with glioblastoma: a retrospective analysis of a large populational database
Bibliographic record
Abstract
Background: The majority of patients diagnosed with glioblastoma develop recurrent disease resulting in poor prognoses. The current study aimed to determine the survival rates of patients diagnosed with glioblastoma in Brazil accounting for the influence of age, treatment modalities, public and private practices, and educational level using a population-based national database. Methods: Patients diagnosed with glioblastoma from 1999-2020 were identified from The Fundação Oncocentro de São Paulo database to create a retrospective cohort. Patients were described according to age, education level treatment modalities and medical practice. In a Cox proportional hazards model, controlled for confounding factors for overall survival, the hazard ratio and 95% CI of overall survival in adults was evaluated. Findings: A total of 4,511 patients were included. The median lengths of survival for patients treated in the public and private settings were 8 and 17 months (p<0.001), respectively. Young patients had longer median overall survival (OS: 18 to 40 years, 41 to 60 years, 61 to 65 years, 66 to 70 years and over than 70 years was 22 months, 10 months, 6 months, 5 months, 4 months, respectively (p<0.001). In general, combined treatments were associated with higher median survival compared to monotherapy. The higher educational level, the higher median survival was observed (4 months for illiterate versus 14 months for university degree). In the multivariable analyses, the significant independent predictors for overall survival were practice setting, educational level, age and treatment modalities. Interpretation: Public practice, older patients, less intensive treatment, and lower educational level were associated with worse survival outcomes in Brazilian glioblastoma patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".