Differences in trends in discharge location in a cohort of hospitalized patients with cancer and non-cancer diagnoses receiving specialist palliative care: A retrospective cohort study
Bibliographic record
Abstract
Background: Patients with and without cancer are frequently hospitalized, and have specialist palliative care needs. In-hospital mortality can serve as a quality indicator of acute care. Trends in acute care outcomes have not previously been evaluated in patients with confirmed specialist palliative care needs or between diagnostic groups. Aim: To compare trends in discharge location between hospitalized patients with and without cancer who received specialist palliative care. Design: Retrospective cohort study. Association between diagnosis (cancer, non-cancer) and in-hospital mortality was assessed using multivariable logistic regression, controlling for demographic, clinical, and admission-specific information. Setting/participants: Patients who received specialist palliative care at an academic tertiary hospital in Toronto, Canada from 2013 to 2019. Results: The cohort comprised 6846 patients, 5024 with and 1822 without cancer. A higher proportion of patients without cancer had a Palliative Performance Scale score <30%, anticipated prognosis of <1 month, and were referred for end-of-life care (all p < 0.001). The adjusted odds of dying in hospital was 1.24-times higher among patients without cancer (95% CI: 1.05–1.46; p = 0.011). Though the proportion of patients without cancer who died in hospital decreased by 8.4% from 2013 to 2019, this proportion (41.2%) remained substantially higher compared to patients with cancer (14.0%) in 2019. Conclusions: Hospitalized patients without cancer were referred to specialist palliative care at a lower functional status, a poorer anticipated prognosis, and more likely for end-of-life care; and were more likely to die in hospital. Future studies are required to determine whether a proportion of hospital deaths in patients without cancer represent goal-discordant end-of-life care.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".