Arthroscopic Shoulder Simulation May Improve Short‐Term Speed, Accuracy, and Efficiency of Surgical Movements in Orthopaedic Residents and Fellows: A Systematic Review
Bibliographic record
Abstract
PURPOSE: To systematically review the effectiveness and validity of orthopaedic surgery training using simulation technologies including augmented reality, virtual reality, and/or mixed reality within arthroscopic shoulder surgery. METHODS: A literature search was conducted of the EMBASE and PubMed databases from inception to January 2024 per the 2020 Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Included studies described arthroscopic shoulder surgery simulation training among orthopaedic surgery trainees. Exclusion criteria included studies assessing nonarthroscopic and nonshoulder simulations, non-English-language studies, case reports, animal studies, studies with overlapping cohorts, and review articles. Simulation characteristics, performance measurements, and validity assessed were extracted. The Cochrane risk-of-bias tool and Newcastle-Ottawa Scale assessed study quality. Simulation type, validation type, and simulation outcomes were assessed. RESULTS: A total of 15 included articles, published from 2011 to 2021, evaluated 421 residents or fellows and 17 medical students. Virtual reality was used in 40% of studies and mixed reality in 60%. The most common outcomes assessed were time to completion (80%), visualizing and probing task performance (60%), and the Arthroscopic Surgery Skill Evaluation Tool (33.3%). Construct validity was assessed in 46.7% of studies, transfer validity in 26.7%, face validity in 20%, and content validity in 6.7%. Three studies demonstrated improved performance in those undergoing simulation training compared with nonsimulation groups. Two studies (13.3%) demonstrated improved time-to-task completion and decreased camera distance traveled when using simulation training. One study demonstrated that postgraduate year 1 and postgraduate year 5 residents derived the greatest benefit from simulation training. CONCLUSIONS: Arthroscopic shoulder simulation training may benefit the surgical skills of orthopaedic residents of all levels of experience as measured by time-to-completion, accuracy, and efficiency of surgical movements. Simulation training exhibits differences in operative time between more- and less-experienced orthopaedic surgeons and trainees. Virtual reality simulation training may result in more-efficient orthopaedic surgical techniques. LEVEL OF EVIDENCE: Level III, systematic review of level I-III studies.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".