Factors that Promote Success in Home Palliative Care: A Study of a Large Suburban Palliative Care Practice
Bibliographic record
Abstract
It has been repeatedly shown that most people would prefer to die in their own homes. However, many factors affect the feasibility of this choice. This study retrospectively examined the medical and nursing charts of 402 cancer patients who wished to die at home and had been referred to a palliative care service. Of those reviewed, 223 (55%) died at home, while 179 died in hospitals. The presence of more than one caregiver, an increased length of time between diagnosis and referral to a palliative care physician, an increased length of time under that physician's care, older age at referral, home ownership, and race were all significantly associated with home death, as were certain cancer diagnoses. The most compelling of these predictive factors have formed the basis for an evaluation tool, soon to be validated, to help palliative health professionals assess the viability of home-based palliative care culminating in a home death.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".