The Future of General Surgery: Evolving to Meet a Changing Practice
Bibliographic record
Abstract
PURPOSE: Similar to other countries, the practice of General Surgery in Canada has undergone significant evolution over the past 30 years without major changes to the training model. There is growing concern that current General Surgery residency training does not provide the skills required to practice the breadth of General Surgery in all Canadian communities and practice settings. PROCEDURE: Led by a national Task Force on the Future of General Surgery, this project aimed to develop recommendations on the optimal configuration of General Surgery training in Canada. A series of 4 evidence-based sub-studies and a national survey were launched to inform these recommendations. MAIN FINDINGS: Generalized findings from the multiple methods of the project speak to the complexity of the current practice of General Surgery: (1) General surgeons have very different practice patterns depending on the location of practice; (2) General Surgery training offers strong preparation for overall clinical competence; (3) Subspecialized training is a new reality for today's general surgeons; and (4) Generation of the report and recommendations for the future of General Surgery. A total of 4 key recommendations were developed to optimize General Surgery for the 21st century. CONCLUSIONS: This project demonstrated that a high variability of practice dependent on location contrasts with the principles of implementing the same objectives of training for all General Surgery graduates. The overall results of the project have prompted the Royal College to review the training requirements and consider a more "fit for purpose" training scheme, thus ensuring that General Surgery residency training programs would optimally prepare residents for a broad range of practice settings and locations across 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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".