A day in the life of emergency general surgery in Canada: a multicentre observational study
Bibliographic record
Abstract
BACKGROUND: Emergency general surgery (EGS) services are gaining popularity in Canada as systems-based approaches to surgical emergencies. Despite the high volume, acuity and complexity of the patient populations served by EGS services, little has been reported about the services' structure, processes, case mix or outcomes. This study begins a national surveillance effort to define and advance surgical quality in an important and diverse surgical population. METHODS: A national cross-sectional study of EGS services was conducted during a 24-hour period in January 2017 at 14 hospitals across 7 Canadian provinces recruited through the Canadian Association of General Surgeons Acute Care Committee. Patients admitted to the EGS service, new consultations and off-service patients being followed by the EGS service during the study period were included. Patient demographic information and data on operations, procedures and complications were collected. RESULTS: Twelve sites reported resident coverage. Most services did not include trauma. Ten sites had protected operating room time. Overall, 393 patient encounters occurred during the study period (195/386 [50.5%] operative and 191/386 [49.5%] nonoperative), with a mean of 3.8 operations per service. The patient population was complex, with 136 patients (34.6%) having more than 3 comorbidities. There was a wide case mix, including gallbladder disease (69 cases [17.8%]) and appendiceal disease (31 [8.0%]) as well as complex emergencies, such as obstruction (56 [14.5%]) and perforation (23 [5.9%]). CONCLUSION: The characteristics and case mix of these Canadian EGS services are heterogeneous, but all services are busy and provide comprehensive operative and nonoperative care to acutely ill patients with high levels of comorbidity.
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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 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".