Retrospective Analysis of the Integration of Palliative Care Into the Care of Stroke Patients Admitted to a Regional Stroke Center
Bibliographic record
Abstract
Background: Palliative care (PC) aims to enhance the quality of life for patients when confronted with serious illness. As stroke inflicts high morbidity and mortality, the integration of PC within acute stroke care remains an important aspect of quality inpatient care. However, there is a tendency to offer PC to stroke patients only when death appears imminent. We aim to understand why this may be by examining stroke patients admitted to a regional stroke centre who subsequently died and their provision of PC. Methods: We conducted a retrospective single-centre cohort study of patients who died during admission to the regional stroke centre at Sunnybrook Health Sciences Centre (SHSC) in Toronto, Ontario, Canada. Baseline demographics were assessed using means, standard deviations (SD), medians, interquartile ranges (IQR), and proportions. Descriptive statistics, univariate, and multivariate analyses were performed to ascertain relationships between collected variables. Results: Univariate modeling demonstrated that older age, being female, no stroke diagnosis at admission to hospital, ischemic stroke, and comorbidities of cancer or dementia were associated with a higher incidence of palliative medicine consultation (PMC), while admission from an acute care hospital and a Glasgow Coma Scale (GCS) coma classification were associated with a lower incidence of PMC. The multivariate model identified the GCS coma-related category as the only significant factor associated with a higher incidence of death but was non-significantly related to a lower incidence of PMC. Conclusion: These results highlight continued missed opportunities for PC in stroke patients and underscore the need to better optimize PMC.
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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 | 0.001 | 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) | 0.001 | 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".