Mitigating Surgical Skill Decay in Orthopaedics Using Virtual Simulation Learning
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has interrupted orthopaedic training structures for both surgeons and trainees. The concept of skill decay must be considered during inactivity of elective practice. The purpose of this study was to provide an evidence-based curriculum in association with immersive virtual reality (iVR) to prevent skill decay during periods of training cessation and beyond. METHODS: A review of pertinent literature for orthopaedic surgical skill decay was performed. Early experience by faculty instructors and residency and fellowship program directors was gathered from multiple institutions with experience in virtual training methods including iVR. A proposed curriculum for cognitive and manual skill acquisition during COVID-19 was produced from qualitative narrative group opinion. RESULTS: Skill decay can occur on the order of days to months and is dependent on the initial skill level. A novel curriculum for structured continuing medical education during and after periods of surgical disruption including e-learning, virtual meetings, and iVR simulators was produced from expert opinion and based on competency-based curriculum standards. CONCLUSION: Skill decay mitigation strategies should use best available evidence technologies and course structures that satisfy advanced learning concepts. The virtual curriculum including iVR simulators may provide cost-effective solutions to training.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".