Impact of Acute Care Surgery Service on Acute Cholecystitis Management and Outcomes at a Single Canadian Academic Network
Bibliographic record
Abstract
Introduction. Surgery has recently gained a prime position on the global health agenda. However, the global surgery community remains fragmented. We sought to map the Global Surgery Offices (GSOs) in Canada and evaluate the scope of their initiatives.Methods. This is a scoping review of all Canadian GSOs. They were identified through the Canadian Association of General Surgeons and by word of mouth. Surveys were conducted electronically and by phone interviews.Results. A total of seven academic institutions have known GSOs. Six out of seven responded. These GSOs span across five provinces and include six universities: Dalhousie, McGill, McMaster, University of Calgary, University of Alberta and University of British Columbia. Low and middle income countries (LMICs) with involvement included Africa (5/6), Americas (4/6), Eastern Europe (1/6) and Asia (1/6). Most GSOs have multiple partners: governmental organizations (2/6); non-governmental organizations (5/6); private institutions (3/6). Only two have formal partnerships between one another. All offer training in international surgery to Canadian residents and most to Canadian medical students (5/6). Only Half (3/6) offer training to LMIC trainees. Whereas one GSO provides surgical support only, others provide data collection (3/6) and quality improvement initiatives (5/6). All benefit from financial support from their Department of Surgery/Anesthesia, two from private funding and only one from grants and fundraising activities. Conclusion. Despite a unifying commitment to improve surgical care in LMICs, GSO in Canada mostly operate independently of one another. We propose to build an epistemic community of Canadian surgeons involved in global health: the u201cCanadian Global Surgery Initiative.u201d This community could function as a flexible governance structure by providing a platform for networking, sharing of ideas, coordinating initiatives, building research-capacity and obtaining political support and sustainable funding. To ensure a more effective collective action, an additional effort should be made to include all surgical specialties.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".