EDUCATIONAL BENEFITS OF COMPUTER-ASSISTED SURGERY SIMULATIONS AND DIRECTED PRACTICE FOR THE TRAINEE
Bibliographic record
Abstract
Orthopaedic surgery residents typically learn total knee arthroplasty (TKA) through an apprenticeship-type model, which is a necessarily slow process. If residents could learn the required technical and cognitive skills more quickly, they could make better use of reduced hours in the operating room, surgeons could teach at a higher level, patients could have shorter operating times with better outcomes, and the healthcare system would have reduced costs and better-trained surgeons. Surgical skills courses, using artificial bones, have been shown to improve technical and cognitive skills significantly within a couple of days. Computer-assisted surgery (CAS) provides real-time feedback and component position planning, leading to improved alignment and a shorter learning curve. Combining these two approaches challenges the participants to consider the same task in different contexts, promoting cognitive flexibility. We designed a hands-on educational intervention for junior residents incorporating a conventional tibiofemoral TKA station, two different tibiofemoral CAS stations and a conventional and CAS patellar resection station. The same implant system was used in all cases. Both qualitative and quantitative analyses were performed. Qualitatively, structured interviews before and after the course were analysed for recurring themes. Quantitatively, subjects were evaluated on their technical skills in a timed conventional TKA test before and after the course, and on their knowledge and error-detection skills after the course. Their performance was compared to senior residents who performed only the testing. Four themes emerged: increased confidence, improved awareness, deepening knowledge and changed perspectives. The residents9 attitudes to CAS changed from negative before the course to neutral or positive after the course. They expected it to be difficult to use and found that it was easy. They originally distrusted the system, but came to think they would use it for their most difficult cases. The junior resident group improved their task completion rate from 23% to 75% of tasks (p As a result of the course, the residents will be more aware what to focus on in the operating room. High impact educational interventions, promoting cognitive flexibility and including real-time feedback from computer-assisted surgery simulations, would benefit trainees, surgeons, the healthcare system and patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".