Hospitalizations at the End of Life Among Long-Term Care Residents
Bibliographic record
Abstract
BACKGROUND: Concerns have been raised over transfers into acute care hospitals at the end of life. The objective of this study was to examine (a) the extent of and (b) factors related to hospitalization in the last 180 days before death among long-term care (LTC) residents. METHODS: The study included all LTC residents from 60 facilities in the province of Manitoba, Canada, who died in 2003/04 (N = 2,379), with data derived from administrative health care records. Multilevel regression analyses were conducted to examine the relationship between resident and facility characteristics and the following: location of death (in hospital vs the LTC facility); whether individuals were hospitalized in the last 180 days before death; and number of hospital days in the last 180 days. RESULTS: Overall, 19.1% of LTC residents died in hospital; however, 40.7% were hospitalized at least once in the last 6 months before death. Several resident characteristics (age, trajectory group, and level of care) were related to the outcome measures. Living in a not-for-profit LTC facility decreased the odds of dying in hospital (adjusted odds ratio [OR] = 0.589; 95% confidence interval [CI] = 0.402-0.863) or being hospitalized (adjusted OR = 0.647; 95% CI = 0.452-0.926). CONCLUSIONS: Hospitalization at the end of life is common among LTC residents, and the likelihood of hospital transfers is increased for residents who are younger, have organ failure, lower care level needs, as well as among those who live in for-profit facilities. Particular emphasis should, therefore, be placed on targeting these groups to determine the appropriateness of hospital admission and possible ways of reducing transfers.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".