Emergency department return visits and hospital admissions in trauma team assessed patients initially discharged from the emergency department: A population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Many injured patients are transported directly to trauma centers, found to be minimally injured, and discharged directly home from the emergency department (ED). Our objectives were to characterize the short-term outcomes in this discharged patient population and to identify patient factors predictive of ED return visits. METHODS: We conducted a retrospective population-based cohort study using linked administrative data sets involving patients assessed at trauma centers in Ontario, Canada between April 1, 2009, and March 31, 2020. Patients who were assessed by a trauma team and discharged directly home from ED were included. The primary outcome was the percentage of patients with an ED return visit within 14 days. We used multivariate logistic regression analyses to identify patient characteristics predictive of at least one ED return visit. RESULTS: There were 5,550 patients included in the study. A total of 1,004 (18.1%) of patients had at least one ED return visit, but only 100 patients (1.8%) were admitted to hospital following initial discharge. Common reasons for ED return visits included wound care concerns (17.2%), head injury complaints (15.6%), and substance misuse (6.8%). Rural residence (odds ratio [OR], 1.83; 95% CI, 1.45-2.29), history of anxiety disorder (OR, 2.05; 95% CI, 1.54-2.73), high baseline ED usage (OR, 2.58; 95% CI, 2.03-3.28), penetrating injury (OR, 1.42; 95% CI, 1.20-1.68), and extremity fracture (OR, 1.52; 95% CI, 1.24-1.88) predicted return visits. CONCLUSION: Patients discharged directly have high rates of ED return visits but low rates of hospital admission or delayed surgical intervention. Trauma services should expand quality assurance initiatives to capture return visits, understand any gaps in clinical service provision, and aim to minimize unnecessary ED return visits. LEVEL OF EVIDENCE: Prognostic/Epidemiological; Level IV.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".