Association between Consultation by a Comprehensive Integrated Palliative Care Program and Quality of End-of-Life Care in Patients with Advanced Cancer in Edmonton, Canada
Bibliographic record
Abstract
Literature assessing the impact of palliative care (PC) consultation on aggressive care at the end of life (EOL) within a comprehensive integrated PC program is limited. We retrospectively reviewed patients with advanced cancer who received oncological care at a Canadian tertiary center, died between April 2013 and March 2014, and had access to PC consultation in all healthcare settings. Administrative databases were linked, and medical records reviewed. Composite score for aggressive EOL care was calculated, assigning a point for each of the following: ≥2 emergency room visits, ≥2 hospitalizations, hospitalization >14 days, ICU admission, and chemotherapy administration in the last 30 days of life, and hospital death. Multivariable logistic regression was adjusted for age, sex, income, cancer type and PC consultation for ≥1 aggressive EOL care indicator. Of 1414 eligible patients, 1111 (78.6%) received PC consultation. In multivariable analysis, PC consultation was independently associated with lower odds of ≥1 aggressive EOL care indicator (OR 0.49, 95% CI 0.38−0.65, p < 0.001). PC consultation >3 versus ≤3 months before death had a greater effect on lower aggressive EOL care (mean composite score 0.59 versus 0.88, p < 0.001). We add evidence that PC consultation is associated with less aggressive care at the EOL for patients with advanced 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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".