Administrative Outcomes Five Years after Opening an Acute Palliative Care Unit at a Comprehensive Cancer Center
Bibliographic record
Abstract
PURPOSE: We examined administrative outcomes after opening an oncology acute palliative care unit (APCU), to determine attainment of administrative targets related to the unit's function of acute palliation. METHODS: We retrospectively reviewed the administrative database for our APCU for the 5 years following its opening in 2003. Data were abstracted on demographic information, as well as source of admission, primary reason for admission, discharge destination, inpatient death rate, and length of stay. Linear regression and the Cochran-Armitage trend test were used for analysis. In keeping with targets set at the unit's opening, our primary hypotheses were that outpatient admissions, admissions for symptom control, and discharges home would increase over the study period; secondary hypotheses were that length of stay and inpatient death rate would decrease. RESULTS: There were 1748 admissions during the study period: the median age was 64, 54% were women, and the most common cancer sites were gastrointestinal (24%), lung (20%), and gynecologic (13%). There were significant changes for all primary endpoints: outpatient admissions increased from 47% to 70% (p < 0.0001), admissions for symptom control increased from 42% to 75% (p < 0.0001), and discharges home increased from 18% to 39% (p < 0.0001). The secondary end points of death rate and length of stay decreased from 65% to 40% (p < 0.0001) and 12 to 11 days (p = 0.54), respectively. CONCLUSION: Setting and adhering to administrative targets for an APCU is possible with appropriate admission criteria, adequate community resources, and education of patients, families and health professionals regarding the model of 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".