Frequency and Pattern of Emergency Department Visits by Long‐Term Care Residents—A Population‐Based Study
Bibliographic record
Abstract
OBJECTIVES: To obtain population-based estimates of emergency department (ED) visits by long-term care (LTC) residents. DESIGN: Retrospective cohort study using administrative data. SETTING: All LTC facilities in Ontario, Canada. PARTICIPANTS: All LTC residents who visited an ED at least once during a 6-month period. MEASUREMENTS: All ED visits were described using the National Ambulatory Care Reporting System. Two distinct visit types were defined. Potentially preventable visits were defined as those for any ambulatory care sensitive condition; these are conditions for which exacerbations that result in hospital use suggest lack of access to adequate primary care. Low-acuity visits were defined as those triaged as non-urgent at ED registration and ended with return to the LTC facility without hospital admission. RESULTS: Nearly one-quarter of LTC residents visited the ED at least once in 6 months. Of all visits, 24.6% were for a potentially preventable reason, most commonly pneumonia, urinary tract infection, and congestive heart failure. These visits had a high frequency of ambulance transport (90.4%), emergent triage (35.3%), hospital admission (62.4%), and death within 30 days (23.6%). Of all visits, 11.0% were low acuity. Fall-related injury was the most common cause. Low-acuity visits were the shortest (mean length 4.5 +/- 4.0 hours) and had the lowest frequency of death within 30 days (4.3%). CONCLUSION: LTC residents made frequent visits to the ED. The visit types showed distinct patterns that suggest a need for better access to medical care for common conditions and a greater emphasis on fall prevention in LTC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".