Palliative Care and the Aggressiveness of End-of-Life Care in Patients With Advanced Pancreatic Cancer
Bibliographic record
Abstract
BACKGROUND: We examined the impact of palliative care (PC) on aggressiveness of end-of-life care for patients with advanced pancreatic cancer. Measures of aggressive care included chemotherapy within 14 days of death; and at least one intensive care unit (ICU) admission, more than one emergency department (ED) visit, and more than one hospitalization, all within 30 days of death. METHODS: A retrospective population-based cohort study using administrative data was conducted in patients with advanced pancreatic cancer from 2005 to 2010 in Ontario, Canada. Multivariable logistic regression was performed with the above measures of aggressive care as the outcomes of interest and PC as the main exposure, adjusting for covariables. Secondary analyses examined intensity of PC as the main exposure defined in two ways: 1) absolute number of PC visits before the outcome of interest (0, 1, 2, 3+ visits) and 2) monthly rate of PC visits. RESULTS: The cohort included 5381 patients (median survival 75 days); 2816 (52.3%) had received a PC consultation. PC consultation was associated with decreased use of chemotherapy near death (odds ratio [OR] = 0.34, 95% confidence interval [CI] = 0.25 to 0.46); lower risk of ICU admission: OR = 0.12, 95% CI = 0.08 to 0.18; multiple ED visits: OR = 0.19, 95% CI = 0.16 to 0.23; multiple hospitalizations near death: OR = 0.24, 95% CI = 0.19 to 0.31). A per-unit increase in the monthly rate of PC visits was associated with lower odds of aggressive care for all four outcomes. CONCLUSION: PC consultation and a higher intensity of PC were associated with less aggressive care near death in patients with advanced pancreatic cancer.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".