Computer-assisted surgery simulations and directed practice of total knee arthroplasty: Educational benefits to the trainee
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".