Incidence and outcomes of emergency department patients requiring emergency general surgery: a 5-year retrospective cohort study
Bibliographic record
Abstract
AIMS: Patients undergoing emergency general surgery are at high risk of complications and death. Our objectives were to estimate the incidence of emergency general surgery in a Swiss University Hospital, to describe the characteristics and outcomes of patients undergoing such procedures, and to study the impact of age on clinical outcomes. METHODS: This was a retrospective cohort study of adult patients who visited the emergency department (ED) of Geneva University Hospitals between January 2015 and December 2019. Routinely collected data were extracted from electronic medical records. The primary outcome was the incidence of emergency general surgery among patients visiting the emergency department, defined as general surgery within three days of emergency department admission. We also assessed demographic characteristics, mortality, intensive care unit admission and patient disposition. Multivariable log-binomial regression was used to study the associations of age with intensive care unit (ICU) admission, one-year mortality and dependence at discharge. Age was modelled as a continuous variable using restricted cubic splines and we compared older patients (75th percentile) with younger patients (25th percentile). RESULTS: Between January 2015 and December 2019, a total of 310,914 emergency department visits met our inclusion criteria. Among them, 3592 patients underwent emergency general surgery within 3 days of emergency department admission, yielding an annual incidence of 116 events per 10,000 emergency department visits (95% CI: 112-119), with a higher incidence in females and young patients. Overall, 5.3% of patients were admitted to ICU, 7.8% were dependent on rehabilitation or assisted living at discharge and 4.8% were dead after one year. Older patients had a higher risk of ICU admission (adjusted risk ratio (aRR) 2.9 [1.5-5.4]), dependence at discharge (aRR 15.3 [5.5-42.4]) and one-year mortality (aRR 5.4 [2.2-13.4]). CONCLUSION: Emergency department visits resulting in emergency general surgery are frequent, but their incidence decreases with patient age. Mortality, ICU admission and dependence at discharge following emergency general surgery are more frequent in older patients. Taking into account the increased risk for older patients, a shared process is appropriate for making more informed decisions about their options for care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".