Influence of socioeconomic factors on survival after breast cancer—A nationwide cohort study of women diagnosed with breast cancer in Denmark 1983–1999
Bibliographic record
Abstract
The reasons for social inequality in breast cancer survival are far from established. Our study aims to study the importance of a range of socioeconomic factors and comorbid disorders on survival after breast cancer surgery in Denmark where the health care system is tax-funded and uniform. All 25,897 Danish women who underwent protocol-based treatment for breast cancer in 1983-1999 were identified in a clinical database and information on socioeconomic variables and both somatic and psychiatric comorbid disorders was obtained from population-based registries. We used Cox proportional hazards models to estimate the association between socioeconomic position and overall survival and further to analyse breast cancer specific deaths in a competing risk set-up regarding all other causes of death as competing risks. The adjusted hazard ratio (HR) for death was reduced in women with higher education (HR, 0.91; 95% confidence interval (CI), 0.85-0.98), with higher income (HR, 0.93; 95% CI, 0.87-0.98) and with larger dwellings (HR, 0.90; 95% CI, 0.85-0.96 for women living in houses larger than 150 m(2)). Presence of comorbid disorders increased the HR. An interaction between income and comorbid disorders resulting in a 15% lower survival 10 year after primary surgery in poor women with low-risk breast cancer having comorbid conditions ( approximately 65%) compared to rich women with similar breast cancer prognosis and comorbid conditions ( approximately 80%) suggests that part of the explanation for the social inequality in survival after breast cancer surgery in Denmark lies in the access to and/or compliance with management of comorbid conditions in poorer women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".