Start of the COVID-19 Pandemic and Palliative Care Unit Utilization: A Retrospective Cohort Study
Bibliographic record
Abstract
CONTEXT: People with noncancer diagnoses have poorer access to palliative care units (PCUs) or hospices compared to those with cancer diagnoses. The COVID-19 pandemic disrupted how specialist palliative care services were delivered and utilized. OBJECTIVE: To determine the association between the start of the COVID-19 pandemic and PCU/hospice utilization in hospitalized individuals with cancer and noncancer diagnoses with specialist palliative care needs. METHODS: Retrospective cohort study using routinely collected data. Percentages of individuals experiencing each disposition from hospital, including discharge to PCU/hospice, were calculated monthly for the total, cancer, and noncancer cohorts and were analyzed descriptively. Hospitalized individuals with specialist palliative care needs at a single academic hospital in Toronto, Canada from January 1, 2017, to September 31, 2022 (pandemic start was defined as April 1, 2020). RESULTS: The cohort comprised 4349 individuals (median age=78 years; 52.4% female); 3065 (70.5%) and 1284 (29.5%) had cancer and noncancer diagnoses, respectively. Among individuals with noncancer diagnoses, the most significant absolute changes were a 13.0%-decrease in in-hospital deaths (prepandemic=49.6%; postpandemic=36.6%) and a 11.6%-increase in discharges to PCU/hospice (prepandemic=35.6%; postpandemic=47.3%). Among individuals with cancer, the most significant absolute changes were a 12.8%-increase in discharges home with formal care (prepandemic=2.3%; postpandemic=15.1%) and a 7.0%-decrease in in-hospital deaths (prepandemic=29.1%; postpandemic=22.0%). CONCLUSION: Despite historically poor PCU/hospice access, the COVID-19 pandemic created circumstances that may have enabled unprecedented utilization in individuals with noncancer diagnoses in our cohort.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".