Global neurosurgery: models for international surgical education and collaboration at one university
Bibliographic record
Abstract
OBJECTIVE: International collaborations between high-income (HICs) and low- and middle-income countries (LMICs) have been developed as an attempt to reduce the inequalities in surgical care around the world. In this paper the authors review different models for international surgical education and describe projects developed by the Division of Neurosurgery at the University of Toronto in this field. METHODS: The authors conducted a review of models of international surgical education reported in the literature in the last 15 years. Previous publications on global neurosurgery reported by the Division of Neurosurgery at the University of Toronto were reviewed to exemplify the applications and challenges of international surgical collaborations. RESULTS: The most common models for international surgical education and collaboration include international surgical missions, long-term international partnerships, fellowship training models, and online surgical education. Development of such collaborations involves different challenges, including limited time availability, scarce funding/resources, sociocultural barriers, ethical challenges, and lack of organizational support. Of note, evaluation of outcomes of international surgical projects remains limited, and the development and application of assessment tools, such as the recently proposed Framework for the Assessment of International Surgical Success (FAIRNeSS), is encouraged. CONCLUSIONS: Actions to reduce inequality in surgical care should be implemented around the world. Different models can be used for bilateral exchange of knowledge and improvement of surgical care delivery in regions where there is poor access to surgical care. Implementation of global neurosurgery initiatives faces multiple limitations that can be ameliorated if systematic changes occur, such as the development of academic positions in global surgery, careful selection of participant centers, governmental and nongovernmental financial support, and routine application of outcome evaluation for international surgical collaborations.
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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 | 0.028 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".