Formal Orthopaedic Surgery “Boot Camp” Curriculum to Optimize Performance on Acting Internships
Bibliographic record
Abstract
Orthopaedic surgery is one of the most competitive residency specialties in the National Residency Matching Program. To improve the odds of matching, senior medical students applying in the field participate in orthopaedic surgery away rotations with programs across the country. Students who do well on these rotations have a higher likelihood of matching because clinical performance is a principal criterion used by admissions committees to rank applicants. On the other hand, these rotations can be physically and emotionally taxing on medical students because poor performance can negatively affect their application and, thus, chances of matching at that institution. Unfortunately, the resources provided by medical schools to prepare students for these high-stakes rotations are usually sparse and unstructured. To address this gap in training at our institution, we developed a formal "boot camp" offered through the university to prepare interested senior medical students for their orthopaedic surgery acting internships. This course focuses on building a solid foundation of musculoskeletal knowledge and exposing students to surgical and procedural skills that are fundamental to the practice of orthopaedic surgery. Over the 2 years, this course has been offered at our institution, and it has proven successful in outcome measures, such as student satisfaction and preparedness, student orthopaedic knowledge, program director evaluations, and match rate. This article describes the novel 1-month curriculum, which includes lectures, laboratory, and clinical experience.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".