Factors associated with acute care use among nursing home residents dying of cancer: a population-based study
Bibliographic record
Abstract
BACKGROUND: Little is known about residents of long-term care (LTC) facilities who die of cancer. The authors examined factors among this cohort prognostic of greater acute care use to identify areas for improving support in LTC. METHODS: The authors used administrative data representing all cancer decedents in Ontario, Canada, who had been living in LTC. Binary logistic regression was used to examine the contribution of covariates to having an emergency department (ED) visit in the last 6 months of life or to death in hospital. RESULTS: Among the 1196 LTC residents in the study cohort, 61% had visited an ED in the last 6 months of life and 20% had died in hospital. Cancer type, income, gender, time in LTC and rural location were not strong predictors of the acute care outcomes. However, certain comorbidities, being younger and region of residence significantly increased the odds of an ED visit and/or hospital death (all P<0.05). CONCLUSIONS: Determining the characteristics of LTC patients more likely to access acute care services can help to inform interventions that avoid costly and potentially adverse transfers to hospital. The study of cancer patients in LTC represents a starting point for clarifying the potential of specialised palliative care nursing and other support that is often lacking in these facilities.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".