Access to Palliative Care during a Terminal Hospitalization
Bibliographic record
Abstract
Background: Research shows that access to palliative care can help patients avoid dying in hospital. However, access to palliative care services during the terminal hospitalization, specifically, has not been well studied. Objective: To determine whether access to palliative care varied by disease trajectory among terminal hospitalizations. Design, Setting, Subjects: We conducted a population-based retrospective cohort study of decedents who died in hospital in Ontario, Canada between 2012 and 2015 by using linked administrative databases. Measurements: Using hospital and physician billing codes, we classified access to palliative care in three mutually exclusive groups of patients with terminal hospitalization: (1) main diagnosis for admission was palliative care; (2) main diagnosis was not palliative care, but the patient received palliative care specialist consultation; and (3) the patient did not receive any specialist palliative care. We conducted a logistic regression on odds of never receiving palliative care. Results: We identified 140,475 decedents who died in an inpatient hospital unit, which represents 42% of deaths. Among inpatient hospital deaths, 23% ( n = 32,168) had palliative care listed as the main diagnosis for admission, 41% ( n = 58,210) received specialist palliative care consultation, and 36% ( n = 50,097) never had access to specialist palliative care. In our regression, dying of organ failure or frailty compared with cancer increased the odds of never receiving palliative care by 4.07 (95% confidence interval [CI]: 3.95–4.20) and 4.51 (95% CI: 4.35–4.68) times, respectively. Conclusions: A third of hospital deaths had no palliative care involvement. Access to specialist palliative care is particularly lower for noncancer decedents. Inpatient units play an important role in providing 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".