The relationship between socioeconomic status and treatment for prostate cancer in a universal healthcare system: A population-based analysis.
Bibliographic record
Abstract
361 Background: A large body of research has shown that there are strong socioeconomic disparities in access to cancer treatment. However, whether these inequalities persist among men with prostate cancer has not been previously explored in the equal-access, universal Canadian health care system. The aim of this study is to compare whether socioeconomic status is associated with the type of treatment received (radical prostatectomy (RP) versus radiation therapy (RT)) for men diagnosed with nonmetastatic prostate cancer in Manitoba, Canada. Methods: Men who were diagnosed with non-metastatic prostate cancer between 2004 and 2016 and subsequently treated with RP or RT were identified using the CancerCare Manitoba Registry and linked to provincial databases. SES was defined as neighbourhood income by postal code and divided into income quintiles (Q1-Q5, with Q1 the lowest quintile and Q5 the highest). Multivariable logistic regression nested models were used to compare whether socioeconomic status was associated with treatment type received. Results: We identified 4,560 individuals between 2004-2016 who were diagnosed with non-metastatic prostate cancer. 2,554 men were treated with RP and 2,006 with RT.As income quintile increased, men were more likely to undergo RP than RT (Q3 vs Q1: aOR 1.45 (1.09-1.92); Q5 vs. Q1: aOR 2.17, 95% CI 1.52-2.86). Conclusions: Despite a universal health care system, socioeconomic inequities are present for men seeking primary treatment for prostate cancer. Further investigation into the decision making process among patients diagnosed with prostate cancer may inform decision making to ameliorate these disparities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".