Impact of opening an acute palliative care unit on administrative outcomes for a general oncology ward
Bibliographic record
Abstract
BACKGROUND: Acute palliative care units (APCUs) are gaining popularity in tertiary care centers. In this study, the authors examined the impact of opening an APCU on administrative outcomes for a general oncology ward (GOW) at a comprehensive cancer center. METHODS: The GOW database was reviewed for 3 periods: June 2000 through May 2002 (before the APCU opened), June 2002 through May 2004 (transitional period, including APCU opening in a temporary location), and June 2004 through May 2006 (after opening of the APCU). Data were extracted on demographics, reasons for admission, admission type, waiting time for admission, length of stay (LOS), overstay (>2 days over expected LOS), death rate, and discharge destination. Linear regression analysis and the Cochran-Armitage test were used for data analysis. RESULTS: There were 5340 admissions: The median patient age was 60 years, and 55% of patients were women. The most common primary cancers were head and neck (22%), gynecologic (21%), gastrointestinal (13%), and lung (12%). There were significant trends on the GOW in decreased admissions for palliative care (12.2%, 9.6%, and 7.9%, respectively, for the 3 periods; P < .0001), fewer inpatient deaths (11.4%, 8.6%, and 6.1%, respectively; P < .0001), and fewer patients with prolonged waits for a bed on a palliative care unit (3.4%, 3%, and 1.7%, respectively; P = .002). Admissions increased for interventions (10.4%, 17.3%, and 22.5%, respectively, for the 3 periods; P < .0001) and for chemotherapy (6.8%, 6.6%, and 9.7%, respectively; P = .001). CONCLUSIONS: After the opening of an APCU at the authors' cancer center, the GOW experienced a decrease in administrative endpoints related to palliative and end-of-life care and an increase in endpoints related to cancer-directed interventions. Prospective studies with clinical endpoints will be required to determine whether this specialization of inpatient care improves quality of life, quality of death, and psychosocial well being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".