Demographic Profile and Utilization Statistics of a Canadian Inpatient Palliative Care Unit within a Tertiary Care Setting
Bibliographic record
Abstract
BACKGROUND: Canadian data describing inpatient palliative care unit (PCU) utilization are scarce. In the present study, we performed a quality assessment of a 24-bed short-term PCU with a 3-months-or-less life expectancy policy in a tertiary care setting. METHODS: Using a retrospective chart review, we explored wait time (wt) for admission (May 2005 to April 2006), length of stay [los (February 2005 to January 2006)], and patient demographics. RESULTS: The wt data showed 508 referrals, with 242 resulting in admissions (92% malignant diagnoses) and 266 not (82% malignant). The most common malignancies in both groups were gastrointestinal, lung, and genitourinary. Median wt for admitted patients was 6 days, varying with referral source, such as the same hospital, home, or another hospital (6, 4, and 8.5 days respectively). Most admissions (93%) occurred in 21 or fewer days. Patient death (52%), admission to another PCU (25%), and declined offer (10%) were common reasons for no admission. Median los for 219 admitted patients was 19 days (range: 0-249 days). Most patients (94%) died in the PCU; a minority were discharged. CONCLUSIONS: Many patients requiring PCU services are admitted within a few days of referral, especially patients with the least available support: those at home. However, half of the non-admitted patients die while waiting-a potential area for improvement. The los for admitted patients complied with the 3-month "expected lifespan" PCU policy. Results are significant, because ensuring quality of life for palliative care patients includes timely PCU access and sufficient los to address end-of-life needs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".