<p>Access to palliative care for cancer patients between diagnosis and death: a national cohort study</p>
Bibliographic record
Abstract
Background and purpose: Introducing palliative care earlier in the disease trajectory has been found to provide better management of physical and psychological suffering. In France, the proportion of cancer patients who receive palliative care is unclear. This study aimed primarily to measure the prevalence of access to inpatient palliative care and associated patient-level factors, and to identify the time between access to palliative care and death. Patients and methods: A nationwide retrospective cohort study using data from the French national health system database (SNDS). All those diagnosed with cancer in 2013 who died between 2013 and 2015 were included. Access to inpatient palliative care was the main outcome. Results: Of the 313,059 patients diagnosed with cancer in 2013 in France, 72,315 (23%) died between 2013 and 2015. Overall, 57% had access to inpatient palliative care. The following groups were the most likely to have access to palliative care: women (adjusted odds ratio, aOR: 1.15; 95% CI: 1.11–1.20), people aged 18–49 (aOR: 1.38; 95% CI: 1.26–1.51), individuals with metastatic cancer (aOR: 2.04; 95% CI: 1.96–2.13), and patients with cancer of the nervous system (aOR: 1.80; 95% CI: 1.62–2.01). The median time between palliative care and death was 29 (interquartile range: 13–67) days. Conclusion: More than half of cancer patients who died within 2 years after diagnosis had access to inpatient palliative care. Access to palliative care occurs late in the disease trajectory, often during the final month of life. Further research and guidelines are warranted to optimize access to early, standardized palliative care. Keywords: French national health system database, palliative care, cancer, death, factors, timing
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".