Are long-term care residents referred appropriately to hospital emergency departments?
Bibliographic record
Abstract
OBJECTIVE: To explore the rate of referrals of long-term care (LTC) residents to emergency departments (EDs) and to determine the appropriateness of the referrals. DESIGN: Retrospective analysis of 2 administrative data sets, paramedic records and hospital records, for the year 2000. SETTING: Catchment area of Hamilton, Ont. PARTICIPANTS: Nineteen LTC facilities and 3 EDs of Hamilton Health Sciences. MAIN OUTCOME MEASURES: Number and appropriateness of referrals were the main outcomes measured; we also examined the timing of and reasons for referrals, arrival status of patients, admissions to hospital, referrals to specialists, and treatments. Unit of analysis was the referral. As no evidence-based guidelines exist for appropriateness of referral, we defined appropriateness as a balance of issues with blinded physician judgment calls on anonymous random subsamples of patients admitted to hospital and those not admitted to determine appropriateness of referrals. Descriptive statistics were used, as well as chi and t tests. RESULTS: Out of 2473 licensed LTC beds, 606 residents were referred to 1 of 3 EDs of the Hamilton Health Sciences hospitals, giving a referral rate of 24.5%. The average age of these LTC residents was 81.6 years, and 63.2% were women. Peak referral months were late winter; peak days were Tuesday and Friday. Time of arrival to the EDs was reported in 6-hour segments, with just over half (51.2%) of residents arriving during the day and one-third in the evening. Respiratory and cardiovascular problems comprised 48.6% of referrals. At arrival 67.3% of cases were deemed urgent or emergent. Wait times ranged from 0 to 60 hours, with 25% of residents seen within 1 hour, 44% within 2 hours, and 50% within 4 hours. Two-thirds (66.7%) of residents were admitted to hospital and of these 62% stayed 1 week. CONCLUSION: Our results agree with previous studies that cast doubt on the idea that LTC residents are "dumped" on EDs. Most referrals appeared appropriate as defined by criteria established by the physician team and given the number of hospital admissions, diagnostic tests, and treatments provided. Potentially, more acute care could be provided in LTC facilities with enhancement of services. Prospective studies could tell us more.
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".