Sufficient Competence to Enter the Unsupervised Practice of Orthopaedics: What Is It, When Does It Occur, and Do We Know It When We See It?
Bibliographic record
Abstract
The goal of residency programs is to provide an educational venue with graduated responsibility and increasing levels of independence as preparation for entering the unsupervised practice of medicine. Surgical programs are required to both cultivate and convey skills pursuant to three fundamental domains: a sufficient fund of knowledge, technical competence in surgical procedures, and a degree of professionalism to enable ethical independent practice. Never before has the expectation that residency programs provide graduated responsibility in preparation for entering the unsupervised practice of medicine been so clearly articulated as it has by Nasca in the recent Accreditation Council for Graduate Medical Education (ACGME) work-hour guideline revisions. The Royal College of Physicians and Surgeons has provided similar guidance in Canada. Yet, as we progress further into the second decade of work-hour restrictions, it is unclear that we have adequately defined or can recognize the critical end points essential to trainee competency. What is clear is that we must achieve these end points in a manner different from that prior to the introduction of work-hour restrictions. We present the current state of thinking from North America and contrast this with the evolving medical educational process in the United Kingdom.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".