The emergency to home project: impact of an emergency department care coordinator on hospital admission and emergency department utilization among seniors
Bibliographic record
Abstract
BACKGROUND: Seniors comprise 14% to 21% of all emergency department (ED) visits, yet are disproportionately larger users of ED and inpatient resources. ED care coordinators (EDCCs) target seniors at risk for functional decline and connect them to home care and other community services in hopes of avoiding hospitalization. The goal of this study was to measure the association between the presence of EDCCs and admission rates for seniors aged ≥ 65. Secondary outcomes included length of stay, recidivism at 30 days, and revisit resulting in admission at 30 days. METHODS: This was a matched pairs study using administrative data from eight EDs in six Alberta cities. Four of these hospitals were intervention sites, in which patients were seen by an EDCC, while the other four sites had no EDCC presence. All seniors aged ≥ 65 with a discharge diagnosis of fall or musculoskeletal pathology were included. Cases were matched by CTAS category, age, gender, mode of arrival, and home living environment. McNemar's test for matched pairs was used to compare admission and recidivism rates at EDCC and non-EDCC hospitals. A paired t-test was used to compare length of stay between groups. RESULTS: There were no statistically significant differences for baseline admission rate, revisit rate at 30 days, and readmission rate at 30 days between EDCC and non-EDCC patients. CONCLUSIONS: This study showed no reduction in senior patients' admission rates, recidivism at 30 days, or hospital length of stay when comparing seniors seen by an EDCC with those not seen by an EDCC.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".