Population-level trends in emergency general surgery presentations and mortality over time
Bibliographic record
Abstract
Emergency general surgery (EGS) refers to the treatment of a subset of general surgical disease necessitating emergency evaluation and management (operative or non-operative)1. The majority of these disorders relate to the abdomen and gastrointestinal tract, with a range of severity. Operations for EGS conditions have higher rates of perioperative complications and death than comparable elective procedures, and many of these deaths are thought to be preventable2,3. There are limited population-level data regarding the impact of EGS. Existing studies4–6 suggest a trend towards decreased mortality with time. Many of these studies relied on incomplete population data that were weighted to estimate more comprehensive regional or national information5,6. Additionally, previous evaluations were largely limited to admitted patients. Emergency department (ED) overcrowding is a worsening public health problem, with potential effects on timely access to care and clinical outcomes7. Patients may be diverted to outpatient surgery, develop complications that change their admitting diagnosis, or even die. Therefore, evaluating EGS-related ED presentations and management even without admission is important. The contribution of individual EGS diagnoses to overall trends in ED presentations, admissions, and mortality is also largely unexplored, yet this information will be essential in anticipating the future needs of the EGS workforce and associated distribution of resources. The objective of this study was to determine the population-level trends in ED presentations, hospital admissions, and mortality from EGS conditions over 17 years in Ontario, Canada.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".