Long-term inequalities in breast cancer survival – a ten year follow-up study of patients managed within a National Health Care System (Sweden)
Bibliographic record
Abstract
INTRODUCTION: Converging epidemiological evidence based on studies of different designs in a variety of populations and settings show that cancer survival tends to be poorer in low compared to high socioeconomic groups. In an extension of an earlier register-based study, we examined the influence of socioeconomic factors on long-term survival in women with a first diagnosis of invasive breast cancer in 1993 in Sweden, a country with a policy of providing equal access to health care to all at nominal cost within a National Health Care System. MATERIAL AND METHODS: The study was based on data set generated by record linkages between the Swedish Cancer Register, Census databases and the Cause of Death Register. Four different categorical variables were used as indicators of socioeconomic standing. Cox proportional hazard regression models were used to estimate the effects of socioeconomic status on risk of death. RESULTS: Of 4 645 eligible women with breast cancer, 1 016 had died from breast cancer at the end of follow-up on December 31, 2003. After adjustment for tumour size and age at diagnosis, risk of death was 19% lower among women belonging to a household of high compared to low socioeconomic status (HR high versus low 0.81; 95% CI: 0.67-0.97). DISCUSSION: These findings indicate that social inequalities in breast cancer survival persist at least up to ten years after an initial diagnosis. While social gradients detected shortly after diagnosis may mainly reflect an influence of socioeconomic differences in overall health status and frailty, differentials persisting beyond five years rather point to a long-term influence of disparities in management of both primary tumours and recurrences. Further studies are needed to explore whether the present findings reflect amendable inequalities in access to state-of-the-art treatment. For all calendar periods, observed survival in the most privileged groups sets the goal for what is achievable for all breast cancer patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".