Predictors of Home Death in Palliative Care Cancer Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it