Older Emergency Department Patients: Does Baseline Care Status Matter?
Bibliographic record
Abstract
BACKGROUND: Little is known about the prognostic differences between older emergency department (ED) patients who present with different formal support requirements in the community. We set out to describe and compare the patient profiles and patterns of health service use among three older ED cohorts: home care clients, nursing home residents and those receiving no formal support. METHODS: We conducted a secondary analysis of the Canadian cohort from the interRAI multinational ED study. Data were collected using interRAI ED contact assessment on patients 75 years of age and older (n = 2,274), in eight ED sites across Canada. A series of descriptive statistics were reported. Adjusted associations were determined using logistic regression. RESULTS: Older adults receiving no formal support services were most stable. However, they were most likely to be hospitalized. Older home care clients were most likely to report depressive symptoms and distressed caregivers. They also had the greatest odds of frequent ED visitation post-discharge (OR=1.9; 95% CI=1.39-2.59). Older adults transferred from a nursing home were the frailest but had the lowest odds of hospital admission (OR=0.14; 95% CI=0.09-0.23). CONCLUSION: We demonstrated the importance of inquiring about community-based formal support services and provide data to support decision-making in the ED.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".