Increasing our understanding of dying of breast cancer: Comorbidities and care
Bibliographic record
Abstract
Background: Screening and treatment for breast cancer have improved. However, attention to palliative support and non-cancer co-morbidities has been limited. This study identified types of care for and co-morbidities of persons dying of breast cancer compared to persons dying from all cancers and from non-cancer causes.Methods: Linked administrative data from population-based registries were used to examine 121,458 deaths in Nova Scotia from 1995 to 2009.Results: Breast cancer decedents' mean age was similar to that of all cancer decedents (72.0 versus 72.1 years), but their age spread was greater (20–59 years: 23.1% versus 16.7%; 90+ years: 11.2% versus 6.5%). Among women dying of breast cancer, 15.6% were enrolled in the diabetes registry and 15.1% in the cardiovascular registry, indicating that they had these non-cancer conditions prior to their death. Compared to all cancer decedents, breast cancer decedents were twice as likely to have dementia as a cause of death, and were less likely to die in hospital but more likely to die in a nursing home. Breast cancer decedents had place of death rates more similar to non-cancer than cancer decedents.Conclusions: Rates of dementia and diabetes among the breast cancer decedents were particularly note-worthy in this novel study given that these comorbidities have not received much attention in the breast cancer research literature. Further collaboration with non-cancer disease programs is advised. The extent of adequate comprehensive palliative support for the 20% of the breast cancer decedents who are nursing home residents requires investigation.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".