Computer-assisted surgery simulations and directed practice of total knee arthroplasty: Educational benefits to the trainee
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Orthopaedic residents typically learn to perform total knee arthroplasty (TKA) through an apprenticeship-type model, which is a necessarily slow process. Surgical skills courses, using artificial bones, have been shown to improve technical and cognitive skills significantly within a couple of days. The addition of computer-assisted surgery (CAS) simulations challenges the participants to consider the same task in a different context, promoting cognitive flexibility. We designed a hands-on educational intervention for junior residents with a conventional tibiofemoral TKA station, two different tibiofemoral CAS stations, and a CAS and conventional patellar resection station, including both qualitative and quantitative analyses. Qualitatively, structured interviews before and after the course were analyzed for recurring themes. Quantitatively, subjects were evaluated on their technical skills before and after the course, and on a multiple-choice knowledge test and error detection test after the course, in comparison to senior residents who performed only the testing. Four themes emerged: confidence, awareness, deepening knowledge and changed perspectives. The residents' attitudes to CAS changed from negative before the course to neutral or positive afterwards. The junior resident group completed 23% of tasks in the pre-course skills test and 75% of tasks on the post-test (p<0.01), compared to 45% of tasks completed by the senior resident group. High-impact educational interventions, promoting cognitive flexibility, would benefit trainees, attending surgeons, the healthcare system and patients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it