Factors associated with multiple transitions in care during the end of life following enrollment in a comprehensive palliative care program
Bibliographic record
Abstract
BACKGROUND: Patients often experience changes or transitions in where and by whom they are cared for at the end of life. These cause stress for both patients and families. Although not all transitions during the end of life can be avoided, advance identification of those who could potentially experience numerous transitions may allow providers and caregivers to anticipate the problem and consider strategies to minimize their occurrence. This study examines the relationship between patient characteristics and the total number of transitions experienced by the patient from the date of admission to a palliative care program (PCP) to death and during final weeks of life. METHODS: Subjects included all adults registered with the PCP in Halifax, Nova Scotia, Canada between 1998 and 2002 and who had died during that period. Data was extracted from the regional PCP database and linked to census information. Transitions were defined as either: 1) a change in location of where the patient was cared for; or 2) a change in which service (specialist groupings, primary care, etc) provided care. Descriptive statistics were calculated plus rate ratios for the association between patient characteristics and total number of transitions. RESULTS: In total, 3972 patients made 5903 transitions during the study period. Although 28% experienced no transitions, over 40% experienced one and 6.3% five or more. At least one transition was made by 47% during the last four weeks of life. Adjusted results suggest women, the elderly and more recent death are associated with experiencing fewer transitions. Multiple transitions were associated with a hospital death and a cancer diagnosis. During the last month of life, age was no longer associated with the total number of transitions, cancer patients were found to experience a similar number or fewer transitions than patients with a non-cancer diagnosis and pain and symptom control become a significant factor associated with a greater number of transitions. CONCLUSION: Our data suggest there is some variation in the number of transitions associated with the demographics and diagnoses of patients. Associations with gender and age require further exploration as does the contribution of caregiver supports and symptom issues.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".