Bibliographic record
Abstract
Background: Dedicated emergency general surgery (EGS) services have been established across North America as a means to bring focus and quality to a large, complex and vulnerable surgical population. The emergence of these services represents a great opportunity to understand and improve emergency surgical care. Methods: This research programs applies a health systems structure/process/outcomes framework to the study of EGS services in Canada: 1. OUTCOME: A systematic review of the effects of an EGS service on patient and non-patient related outcomes 2. STRUCTURE: A national cross sectional study of structure and case mix on 14 EGS services 3. PROCESS: Detailed process mapping of a complex EGS condition Results: 1. OUTCOMES: Studies found increased daytime and decreased after-hours operating, improved patient transit from ED to OR to home, and decreased length of stay after implementation of an EGS service. The overall trend was higher more diverse case volumes, which improved resident education. Lower complication rates were noticed in the appendicitis and cholecystitis groups. 2. STRUCTURE: Canadian EGS services demonstrated variability in service organization and access to operating rooms. However, a national cross sectional study of EGS patients revealed that all services see diverse case mix and high complexity, and routinely make complex judgments about operative and non-operative care. 3. PROCESS: The processes of care for small bowel obstruction (SBO) patients from the time of presentation to the time of follow-up were highly elaborate and variable in terms of duration. Data visualization strategies were used to identify substantial variability in terms of time to CT scan and time to OR. Conclusions: The EGS model has been implemented worldwide, and has demonstrated an improvement in timeliness of care, decreased administrative costs, and improved trainee learning. EGS services are well-established in Canada, and poised to identify new opportunities for improved patient care. Process mapping has been successfully integrated into surgical specialties and provides insight into potential areas of performance improvement in EGS.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".