P.179 An International Comparison of Neurosurgical Competence by Design Curriculum
Bibliographic record
Abstract
Background: Prior to its recent introduction into Canadian neurosurgical curriculum, Competence by Design (CBD) principles have been implemented across many international neurosurgical training programs for several years. As such, comparing other international competency-based educational frameworks and curricula can help anticipate, avoid or mitigate potential future challenges for Canadian neurosurgical trainees. Methods: A comparative web-based analysis of neurosurgical postgraduate medical education documents and resources provided by medical accreditation and regulatory bodies of Canada, the United States, the United Kingdom and Australasia, was performed. Results: All four countries varied considerably across four major curriculum-based themes: 1) general program structure; 2) overarching foundational competency frameworks; 3) types and numbers of performance assessments required and; 4) curricular learning outcomes. In particular, the expected progression and degree of competence required of neurosurgical residents when performing entrustable professional activities (EPAs) or defined tasks of neurosurgical practice, varied across all countries. Differences in types of neurosurgical EPAs and number of required assessments demonstrating a trainee’s competence achievement were also appreciated. Conclusions: This study revealed variations across competency-based neurosurgical curricula proposed by four international medical training regulatory bodies. Differences in types of EPAs and their required degree of competence achievement suggests potential disconnects between neurosurgical educational outcomes and actual medical practice.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".