Pattern of Care at the End of Life: Does Age Make a Difference in What Happens to Women With Breast Cancer?
Bibliographic record
Abstract
PURPOSE: In the last 40 years, palliative care has become the standard of care at the end of life. However, there are limited data about the degree of access to such care at the population level. METHODS: Using administrative databases, a care-oriented profile score was created to describe the care received during the last 6 months of life for 2,291 women who were dying of breast cancer in the province of Quebec, Canada, during the years 1992 to 1998. The care received was described through indicators of care that would reflect a palliative care philosophy. An ordinal score was developed for comparisons among age groups of women using a proportional odds ordinal regression model. RESULTS: We found that only 6.9% of women died at home, while 69.6% of them died in acute care beds. While most women (75%) had few indicators indicating provision of palliative care during the last 6 months of life, younger women (< 50 years) were even less likely (odds ratio, 0.70; 95% CI, 0.54 to 0.90) to receive such care compared with middle aged women (50 to 59 years; serving as the reference group), while older women (> 70 years) were more likely (odds ratio, 1.85; 95% CI, 1.49 to 2.29). CONCLUSION: Our study indicates that a sizeable proportion of women terminally ill from breast cancer do not have access to palliative care-an issue that health care policy makers may wish to explore further.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".