Can We Agree on Expectations and Assessments of Graduating Residents?
Bibliographic record
Abstract
Orthopaedic educators are responsible for training a prepared and competent workforce that will provide effective care for a growing number of patients with musculoskeletal conditions. Currently, there are both internal and external forces that pose substantial challenges to medical students, residents, program directors, faculty members, and chairs in achieving this goal. One area of particular concern is the education of surgeons, whose knowledge and professional behavior must be matched by their ability to acquire procedural skills. In order to address this issue, many training systems have implemented a competency-based training approach into their curricula. This article discusses the efforts that orthopaedic training bodies in Canada and Australia have taken toward competency-based education and what steps the American Board of Orthopaedic Surgery (ABOS), the Council of Orthopaedic Residency Directors (CORD), the American Orthopaedic Association (AOA), the American Academy of Orthopaedic Surgeons (AAOS), and the Accreditation Council for Graduate Medical Education (ACGME) are considering to improve residency education in the current and future environments.
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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 | 0.026 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".