A lay of the land: a description of academic acute care surgery models in Canada
Bibliographic record
Abstract
BACKGROUND: Patients who require emergency general surgery (EGS) are at a substantially higher risk for perioperative morbidity and mortality than patients undergoing elective general surgery. The acute care surgery (ACS) model has been shown to improve EGS patient outcomes and cost-effectiveness. A recent systematic review has shown extensive heterogeneity in the structure of ACS models worldwide. The objective of this study was to describe the current landscape of ACS models in academic centres across Canada. METHODS: We sent an online questionnaire to the 18 academic centres in Canada. The lead ACS physicians from each institution completed the questionnaire, describing the structure of their ACS models. RESULTS: In total, 16 institutions responded, all of which reported having ACS models, with a total of 29 ACS services described. All services had resident coverage. Of the 29, 18 (62%) had dedicated allied health care staff. The staff surgeon was free from elective duties while covering ACS in 17/29 (59%) services. More than half (15/29; 52%) of the services described protected ACS operating room time, but only 7/15 (47%) had a dedicated ACS room all 5 weekdays. Four of 29 services (14%) had no protected ACS operating room time. Only 1/16 (6%) institutions reported a mandate to conduct ACS research, while 12/16 (75%) found ACS research difficult, owing to lack of resources. CONCLUSION: We saw large variations in the structure of ACS models in academic centres in Canada. The components of ACS models that are most important to patient outcomes remain poorly defined. Future research will focus on defining the necessary cornerstones of ACS models.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".