Survival Duration among Patients with a Noncancer Diagnosis Admitted to a Palliative Care Unit: A Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Palliative care unit (PCU) beds are a limited resource in Canada, so PCU admission is restricted to patients with a short prognosis. Anecdotally, PCUs further restrict admission of patients with noncancer diagnoses out of fear that they will "oversurvive" and reduce bed availability. This raises concerns that noncancer patients have unequal access to PCU resources. PURPOSE/METHODS: To clarify survival duration of patients with a noncancer diagnosis, we conducted a retrospective review of all admissions to four PCUs in Toronto, Canada, over a 1-year period. We measured associations between demographic data, prognosis, Palliative Performance Score (PPS), length of stay (LOS), and waiting time. RESULTS: We collected data for 1000 patients, of whom 21% had noncancer diagnoses. Noncancer patients were older, with shorter prognoses and lower PPS scores on admission. Noncancer patients had shorter LOS (14 versus 24, p<0.001) than cancer patients and a similar likelihood of being discharged alive to cancer patients. Noncancer patients had a trend to lower LOS across a broad range of demographic, diagnostic, prognostic, and PPS categories. Multivariable analysis showed that LOS was not associated with the diagnosis of cancer (p=0.36). DISCUSSION/CONCLUSION: Noncancer patients have a shorter LOS than cancer patients and a similar likelihood of being discharged alive from a PCU than cancer patients, and the diagnosis of cancer did not correlate with survival in our study population. Our findings demonstrate that noncancer patients are not "oversurviving," and that referring physicians and PCUs should not reject or restrict noncancer referrals out of concern that these patients are having a detrimental impact on PCU bed availability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".