Predictors of Home Death in Palliative Care Cancer Patients
Bibliographic record
Abstract
With recent changes in health care there is greater emphasis on providing care at home, including the support of families to enable more home deaths. Since a home death may not be practical or desirable in every family situation, there is a need for an objective way to assess the viability of a home death in each individual family situation. The purpose of this study was to describe the relative role of predictors of home death in a cohort of palliative care patients with advanced cancer. A questionnaire was created as a means of assessing the viability of a home death. Five questions were included. Ninety questionnaires were administered by home care coordinators. A follow-up questionnaire was administered to record the place of death. Of the 73 evaluable patients, 34 (47%) died at home and 39 (53%) died in hospital or hospice. The desire for a home death by both the patient and the caregiver, support of a family physician, and presence of more than one caregiver were all significantly associated with a home death. Logistic regression identified a desire for home death by both the patient and the caregiver as the main predictive factor for a home death. The presence of more than one caregiver was also predictive of home death. The questionnaire is simple and, if our results are confirmed, it can be used for predicting those who will not have a home death.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".