End-of-life cancer care: Health service delivery in the last 12 months of life in Calgary, Alberta, Canada.
Bibliographic record
Abstract
171 Background: In Calgary, Alberta, the Calgary Zone Palliative Care Collaborative (CZPCC) undertook a study to examine the current state of cancer and palliative services and to recommend steps to address gaps in service delivery. We hypothesized that early access to palliative care services would reduce utilization of active cancer treatments and services for individuals nearing the end of life. Our study objectives were to determine the utilization and timing of acute, palliative and oncology-related services in Calgary. Methods: This retrospective study examined cancer registry and administrative data for patients > 18 years, who died in 2012. Measures of aggressive end of life care (EOL) were also collected. A combination of descriptive statistics, tests of association and multivariate regression analysis were conducted. Results: N = 1909 died of cancer in 2012: median age 73 years (IQR: 62-82 years) and median disease duration 364 days (IQR: 92-1114 days). 40.6% of patients received systemic treatment in last 12 months of life. 29.9% received radiotherapy and 13.0% received psychosocial/spiritual care. Palliative care contact was 80.7%, inclusive of 20.6% who had an intensive palliative care unit admission. 5.2% had EOL chemotherapy and 3.8% received EOL radiotherapy. Up to 10.4% of patients had one or more hospital admission. There was no significant effect of age on those who received aggressive EOL care. Men had an increased probability to receive aggressive EOL care (p = 0.015). Tumor group was also associated with receiving aggressive EOL (p < 0.001), with the highest utilization in Head and Neck and hematological malignancies. In patients with a disease duration of > 4 months those who received palliative care at least 2-3 months prior to death were less likely to receive aggressive EOL care (P < 0.001). Patients whose disease duration was < 1 month were less likely to receive aggressive EOL care if they received palliative care services (p = 0.02). Conclusions: The provision of palliative care services at the end of life is most needed among men and certain tumor groups who are the highest users of aggressive EOL care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".