Unscheduled Returns of Older Patients to the Emergency Department of an Irish Teaching Hospital in the Final Quarter of 2023
Bibliographic record
Abstract
Abstract Background Rapid unscheduled returns to emergency departments (ED) are considered to be an indication of a potential lack of quality of care of an ED as outlined in the academic literature. Our ED multidisciplinary team (MDT) dedicated to the assessment and treatment of older persons is a relatively new one. In order to evaluate our efficacy, we sought to compare our rate of unscheduled returns with international norms. Methods Retrospective audit of 883 patients from October 2023 to January 2024. Our ED software programme which holds the patient histories was reviewed to establish whether the patients came back to the ED, if so, how quickly, and for what reason. The results were collated and reviewed. Results Average age: 81. Average Clinical Frailty Scale Score (CFS): 5. 69% of older patients assessed by the MDT did not return to our ED. 3% returned within 72hrs. 2% returned within one week. 9% returned within one month. 13% returned within 3 months. 2% returned within 6 months. Conclusion In numerous academic articles, the 72hr unscheduled return should not exceed 3-5%. While we would always seek to reduce the rate of unscheduled returns to our ED, the results show that our 72hr return percentage is within international norms. This is despite the age profile and frailty of the patients assessed, treated and reviewed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".