Content and Face Validity Assessment of the Sim-K Haptic-Feedback Enhanced Total Knee Replacement Virtual Reality Simulator.
Bibliographic record
Abstract
Objectives Virtual reality simulators for open orthopaedic procedures, such as total knee replacement (TKR), are uncommon compared to arthroscopic or fluoroscopic procedures. The Ossim Sim-K is to our knowledge the first virtual reality TKR simulator with haptic feedback and we sought to investigate the face and content validity of the first iteration of this device. Methods Thirty members of the local orthopaedic department were recruited for this study. After completing a full simulation, each candidate completed a questionnaire utilising a 7-point Likert scale throughout, to assess face and content validity. Candidates were deemed either inexperienced or experienced based on whether they had performed less or more than 40 TKR respectively. Results Questionnaire results were positive for all items related to both face and content validity, though inexperienced surgeons were more positive about the Sim-K in 7 of the 10 face validity items and 2 of the 5 content validity items. Conclusions The Sim-K was well received, particularly by inexperienced surgeons with respect to both face and content validity. The Sim-K represents a promising start in the production of a TKR simulator with haptic feedback. With further development this system has the potential to be valuable in the training of orthopaedic surgeons. Declaration: This work was funded by OSSimTech (Montreal, Canada), developer of the Sim-K TKR Simulator. No authors receive direct funding or have a financial interest in the company.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".