Care trajectories and transitions at the end of life: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: End-of-life periods are often characterised by suboptimal healthcare use (HCU) patterns in persons aged 65 years and older, with negative effects on health and quality of life. Understanding care trajectories (CTs) and transitions in this period can highlight potential areas of improvement, a subject yet only little studied. OBJECTIVE: To propose a typology of CTs, including care transitions, for older individuals in the 2 years preceding death. DESIGN: Retrospective cohort study. METHODS: We used multidimensional state sequence analysis and data from the Care Trajectories-Enriched Data (TorSaDE) cohort, a linkage between a Canadian health survey and Quebec health administrative data. RESULTS: In total, 2080 decedents were categorised into five CT groups. Group 1 demonstrated low HCU until the last few months, whilst group 2 showed low HCU over the first year, followed by a steady increase. A gradual increase over the 2 years was observed for groups 3 and 4, though more pronounced towards the end for group 3. A persistent high HCU was observed for group 5. Groups 2 and 4 had higher proportions of cancer diagnoses and palliative care, as opposed to comorbidities and dementia for groups 3 and 5. Overall, 68.4% of individuals died in a hospital, whilst 27% received palliative care there. Care transitions increased rapidly towards the end, most notably in the last 2 weeks. CONCLUSION: This study provides an understanding of the variability of CTs in the last two years of life, including place of death, a critical step towards quality improvement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".