Toward a Comprehensive Postgraduate Surgical Training Program in Saudi Arabia
Bibliographic record
Abstract
Residency training is a crucial building block for teaching clinical, academic, and professional skills to practice safe and reliable medicine; however, examining the outcome of various training programs has received minimal attention from leading postgraduate medical education journals. Linking the apparent association between surgical training approaches with the quality of postoperative patient outcomes is nearly non-existent. This three-paper dissertation examines the empirical association between the postgraduate surgical training approaches and patient outcomes, using Canada and Saudi Arabia as the two countries of interest. It also examines the influence of hospital center as a confounding variable (case volume, board certification, hospital size), and cost-effectiveness created as a result of being treated by a Saudi surgeon who completed his/ her postgraduate training in Canada vs. postgraduate surgical training in Saudi Arabia, in current Saudi Riyals, which amounts to 9,904,512 Saudi riyals annually in reduction of postoperative complications for fifteen surgeries per month in favor of the Canadian training system. The final paper proposes a quality improvement framework to improve training in postgraduate surgical training programs and in all postgraduate health training programs within the Kingdom. The First Paper analyses the difference in patient outcomes based on the empirical association between two surgical approaches administered in two different countries, Canada and Saudi Arabia, and the influence of hospital center characteristics, using a multi-variant logistic regression analysis. Evidence collected from patient files was analyzed and showed that there are, in fact, differences in patient outcomes based on the board certification training process in favor of the Canadian training system in Cardiac surgery only when factoring in the organizational effect. The Second Paper analyses the incremental cost-effectiveness of training Saudi surgeons nationally vs. training them in Canada. Evidence shows that Canadian-trained Saudi surgeons were more cost-effective in neurosurgery. The Third Paper uses a multi-institutional qualitative method approach to identify key factors that need to be improved in postgraduate medical and surgical programs within Saudi Arabia. Key findings of this paper indicate that the training process's governance was the most crucial aspect that needs to be improved within the postgraduate medical and surgical training programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".