A comparison of orthopaedic surgery training across five English-speaking countries
Bibliographic record
Abstract
© 2024 Royal Australasian College of Surgeons.Introduction: There is undocumented and unjustified variability in orthopaedic surgery training between countries. This study compares and contrasts the main features of orthopaedic training in Australia, New Zealand, the United Kingdom, United States, and Canada. Methods: Comparisons included: competition for, and selection into, training; training pathway structures; training requirements, and; training length. Results: Selection into orthopaedic surgery training is competitive in all countries assessed with acceptance rates ranging from 22%-26% in Australia and New Zealand to 85% in Canada. Minimum length of post-medical school training varies from 5 years in the USA and Canada, to 8 years in Australia, 9 years in New Zealand, and 10 years in the United Kingdom. All countries encourage participation in research during training, although there are varying requirements. Significant bottlenecks characterize selection into training in Australia, New Zealand, and the United Kingdom, meaning the majority of doctors take more than a decade from medical school graduation to obtaining their specialty surgery qualification. Conclusions: There is high variability between the orthopaedic training programs of the studied countries. An awareness of these differences and similarities may help improve training, or provide solutions for identified gaps in each country.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".