Disparities in the survival of endometrial cancer patients in a public healthcare system: A population-based cohort study.
Bibliographic record
Abstract
Objective: Social determinants of health (SDH) have been shown to correlate with adverse cancer outcomes. It is unclear if their impact goes beyond behavioral risk or healthcare access. We aimed to evaluate the association of SDH with endometrial cancer outcomes in a public healthcare system. Design and Setting: A retrospective cohort study of endometrial cancer patients in Ontario, Canada. Population: Women diagnosed with endometrial cancer in Ontario between 2009-2017. Methods: Clinical and sociodemographic variables were extracted from administrative databases. Validated marginalization scores for material deprivation, residential instability and ethnic concentration were used. Associations between marginalization and survival were evaluated using log-rank testing and Cox proportional hazards regression. Results: 20228 women with endometrial cancer were identified. Fewer patients in marginalized communities presented with early disease (70% vs. 76%, p<0.001) and received surgery (89% vs. 93%, p<0.001). Overall survival was shorter among marginalized patients (p<0.001). On multivariable analysis adjusted for patient and disease factors, overall marginalization (HR=1.22, 95% CI 1.03-1.08), material deprivation (HR=1.22, 95% CI 1.10-1.35) and residential instability (HR=1.32, 95% CI 1.19-1.46) were associated with increased risk of death (p<0.001). Conclusions: Socioeconomic marginalization is associated with an increased risk of death in endometrial cancer patients. Targetable events in the cancer care pathway should be identified to improve health equity Funding: This study was supported by a grant (#RD-196) from the Hamilton Health Sciences Juravinski Hospital and Cancer Center Foundation Keywords: uterine cancer, endometrial cancer, social determinants of health
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".