Resident education in orthopaedic trauma
Bibliographic record
Abstract
As residency training programmes around the globe move towards competency-based medical education (CBME), there is a need to review current teaching and assessment practices as they relate to education in orthopaedic trauma. Assessment is the cornerstone of CBME, as it not only helps to determine when a trainee is fit to practice independently, but it also provides feedback on performance and guides the development of competence. Although a standardised core knowledge base for trauma care has been developed by the leading national accreditation bodies and international agencies that teach and perform research in orthopaedic trauma, educators have not yet established optimal methods for assessing trainees' performance in managing orthopaedic trauma patients. This review describes the existing knowledge from the literature on assessment in orthopaedic trauma and highlights initiatives that have recently been undertaken towards CBME in the United Kingdom, Canada and the United States. In order to support a CBME approach, programmes need to improve the frequency and quality of assessments and improve on current formative and summative feedback techniques in order to enhance resident education in orthopaedic trauma. Cite this article: Bone Joint J 2016;98-B:1320-5.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".