Structures, processes and models of care for emergency general surgery in Ontario: a cross-sectional survey
Bibliographic record
Abstract
Background: Emergency general surgery (EGS) patients require urgent surgical evaluation and intervention for various conditions, such as infectious or obstructive diseases of the gastrointestinal tract. We aimed to characterize the structures and processes that are relevant to the delivery of EGS care across Ontario hospitals and to evaluate the availability of critical resources at hospitals with formal EGS models. Methods: Between August 2019 and July 2020, we conducted a cross-sectional survey of Ontario hospitals that offered urgent general surgery (defined as the ability to provide nonelective surgical intervention within 24 to 48 hours of presentation) to adults. People with intimate knowledge of their hospital’s EGS program completed a Web-based or telephone survey characterizing the program’s organizational structure and staffing, operating room availability, interventional radiology and interventional endoscopy availability, intensive care unit availability and staffing, and regional participation. Their responses were compiled and comparisons were made between hospitals with and without formal EGS models of care, as well as between hospitals based on size and academic status. Results: Of the 114 Ontario hospitals identified, 109 responded (95.6% response rate). A third (34.6%; n = 37/107) of hospitals had EGS models of care. Thirty-four of these (91.9%) were large (> 100-bed) institutions that would be likely to have increased resources. However, even for hospitals of similar size, those with EGS models had increased staffing levels compared to those without (clinical associates 17.6% [n = 3/17] v. 10.0% [n = 2/20]; nurse practitioners or physician assistants 27.8% [n = 5/18] v. 14.3% [n = 3/21]). They also had better access to diagnostic and interventional equipment (24/7 access to computed tomography 94.1% [n = 16/17] v. 69.2% [n = 18/26]), interventional radiology (88.9% [n = 16/18] v. 42.3% [n = 11/26]), endoscopy (100% [n = 18/18] v. 69.2% [n = 18/26]) and endoscopic retrograde cholangiopancreatography (77.8% [n = 14/18] v. 42.3% [n = 11/26]), as well as dedicated operating room time (72.2% [n = 13/18] v. 0% [n = 0/25]). Interpretation: The structures and processes available to care for patients requiring EGS in Ontario were highly variable between hospitals. Hospitals with formal EGS models were more likely to have access to key resources.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".