Update on Educational Resources and Evaluation Tools for Orthopaedic Surgery Residents
Bibliographic record
Abstract
Innovations in orthopaedic resident educational resources and evaluation tools are essential to ensuring appropriate training and ultimately the graduation of competent orthopaedic surgeons. In recent years, there have been several advancements in comprehensive educational platforms within orthopaedic surgery. Orthobullets PASS, Journal of Bone and Joint Surgery Clinical Classroom, and American Academy of Orthopaedic Surgery Resident Orthopaedic Core Knowledge each have their own unique advantages in preparation for the Orthopaedic In-Training Examination and American Board of Orthopaedic Surgery board certification examinations. In addition, the Accreditation Council for Graduate Medical Education Milestones 2.0 and the American Board of Orthopaedic Surgery Knowledge Skills Behavior program each provide objective assessment of resident core competencies. Understanding and using these new platforms will help orthopaedic residents, faculty, residency programs, and program leadership to best train and evaluate their residents.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".