Impact of specialist palliative care delivered over three months prior to death on a colorectal cancer patient’s risk of experiencing aggressive end-of-life care.
Bibliographic record
Abstract
6614 Background: More patients are experiencing aggressive end-of-life (EOL) care. This is concerning as aggressive EOL care, on a population level, is associated with poor quality care. Specialist palliative care (PC) has been shown to help relieve EOL symptoms, improve patient quality of life, and reduce aggressive EOL care. This study aimed to estimate the impact of the timing of specialist PC, specifically PC delivered at least 3 months prior to death, on a colorectal cancer (CRC) patient’s risk experiencing aggressive care in the last 30 days of life. Methods: A population-based retrospective cohort study of adult patients who died from CRC in Alberta, Canada from 2011-2015. The Alberta Cancer Registry was used to identify the cohort, which was linked to healthcare resource use data in local, provincial, and national databases. Individuals who died < 30 days from CRC diagnosis were excluded. Patients who accessed any of the provinces specialist PC services were deemed exposed to specialist PC (includes PC consult team, intensive PC unit, palliative home care, hospice). Aggressive EOL care was defined as having experienced at least one of: hospital death, > 1 emergency department visit, > 1 hospital admission, > 14 days of hospitalization, ≥1 intensive care unit admission, ≥1 new chemotherapy program (or any treatment in the last 14 days of life). Logistic regression was used to model factors (specialist PC timing and clinical characteristics) associated with aggressive EOL care. Results: The cohort comprised 2979 patients. Most patients received specialist PC before death (58%); 60% had ≥1 indicator of aggressive EOL care. Relative to patients who received specialist PC > 3 months before death, patients who received specialist PC < 3 months before death were 1.5 times more likely to experience aggressive EOL care (CI: 1.2-1.9). Patients who received no specialist PC were 2.1 times more likely to experience aggressive EOL care (CI: 1.7-2.8). Short disease duration ( < 1 year from diagnosis to death), younger age at death, living in a rural area, and male sex, were also associated with higher odds of experiencing aggressive EOL care. Conclusions: Specialist PC delivered > 3 months before death reduces a CRC patient’s risk of experiencing aggressive EOL care over PC delivered < 3 months before death.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".